21st Century Nanoscience - A Handbook (Volume 10) : Public Policy, Education, and Global Trends [10] 9780815357094, 9780429351631

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21st Century Nanoscience – A Handbook

21st Century Nanoscience – A Handbook: Nanophysics Sourcebook (Volume One) 21st Century Nanoscience – A Handbook: Design Strategies for Synthesis and Fabrication (Volume Two) 21st Century Nanoscience – A Handbook: Advanced Analytic Methods and Instrumentation (Volume Three) 21st Century Nanoscience – A Handbook: Low-Dimensional Materials and Morphologies (Volume Four) 21st Century Nanoscience – A Handbook: Exotic Nanostructures and Quantum Systems (Volume Five) 21st Century Nanoscience – A Handbook: Nanophotonics, Nanoelectronics, and Nanoplasmonics (Volume Six) 21st Century Nanoscience – A Handbook: Bioinspired Systems and Methods (Volume Seven) 21st Century Nanoscience – A Handbook: Nanopharmaceuticals, Nanomedicine, and Food Nanoscience (Volume Eight) 21st Century Nanoscience – A Handbook: Industrial Applications (Volume Nine) 21st Century Nanoscience – A Handbook: Public Policy, Education, and Global Trends (Volume Ten)

21st Century Nanoscience – A Handbook Public Policy, Education, and Global Trends (Volume Ten)

Edited by Klaus D. Sattler

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Library of Congress Cataloging-in-Publication Data Names: Sattler, Klaus D., editor. Title: 21st century nanoscience : a handbook / edited by Klaus D. Sattler. Description: Boca Raton, Florida : CRC Press, [2020] | Includes bibliographical references and index. | Contents: volume 1. Nanophysics sourcebook—volume 2. Design strategies for synthesis and fabrication—volume 3. Advanced analytic methods and instrumentation— volume 5. Exotic nanostructures and quantum systems—volume 6. Nanophotonics, nanoelectronics, and nanoplasmonics—volume 7. Bioinspired systems and methods. | Summary: “This 21st Century Nanoscience Handbook will be the most comprehensive, up-to-date large reference work for the field of nanoscience. Handbook of Nanophysics, by the same editor, published in the fall of 2010, was embraced as the first comprehensive reference to consider both fundamental and applied aspects of nanophysics. This follow-up project has been conceived as a necessary expansion and full update that considers the significant advances made in the field since 2010. It goes well beyond the physics as warranted by recent developments in the field”—Provided by publisher. Identifiers: LCCN 2019024160 (print) | LCCN 2019024161 (ebook) | ISBN 9780815384434 (v. 1 ; hardback) | ISBN 9780815392330 (v. 2 ; hardback) | ISBN 9780815384731 (v. 3 ; hardback) | ISBN 9780815355281 (v. 4 ; hardback) | ISBN 9780815356264 (v. 5 ; hardback) | ISBN 9780815356417 (v. 6 ; hardback) | ISBN 9780815357032 (v. 7 ; hardback) | ISBN 9780815357070 (v. 8 ; hardback) | ISBN 9780815357087 (v. 9 ; hardback) | ISBN 9780815357094 (v. 10 ; hardback) | ISBN 9780367333003 (v. 1 ; ebook) | ISBN 9780367341558 (v. 2 ; ebook) | ISBN 9780429340420 (v. 3 ; ebook) | ISBN 9780429347290 (v. 4 ; ebook) | ISBN 9780429347313 (v. 5 ; ebook) | ISBN 9780429351617 (v. 6 ; ebook) | ISBN 9780429351525 (v. 7 ; ebook) | ISBN 9780429351587 (v. 8 ; ebook) | ISBN 9780429351594 (v. 9 ; ebook) | ISBN 9780429351631 (v. 10 ; ebook) Subjects: LCSH: Nanoscience—Handbooks, manuals, etc. Classification: LCC QC176.8.N35 A22 2020 (print) | LCC QC176.8.N35 (ebook) | DDC 500—dc23 LC record available at https://lccn.loc.gov/2019024160 LC ebook record available at https://lccn.loc.gov/2019024161 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

Contents Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Innovation and Entrepreneurship Ugo Finardi . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy and Innovation: An Invisible Evolving Nanoworld Biancamaria Baroli, Maria Francesca Matzeu, and Carla Serri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The “Mega” Power of the “Nano” Viorel Nicolae Gaftea . . . . . . . . . . . . . . . . . . . . Nanotechnology: History and Future Shalini Chaturvedi and Pragnesh N. Dave . . . . . Training Leaders in Nanotechnology Joshua A. Jackman, Dong-Joon Cho, Jeremy S. Jackman, Aldrin E. Sweeney, and Nam-Joon Cho . . . . . . . . . . . . . . . . . . . Challenges in Nanoscience Education M. Gail Jones, Ron Blonder, and Anna-Leena K¨ahk¨onen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Nanoworlds for Learning Jennifer Flint, Karljohan Lundin Palmerius, Gunnar H¨ost, and Konrad Sch¨onborn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolving Perspectives on Nanoeducation and Capacity Development Ineke Malsch and Frank Kupper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exposing School Students to Nanoscience: A Review of Published Programs Ron Blonder and Ella Yonai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrating Nanoscience in High School Science: Curriculum Models and Instructional Approaches Douglas Huffman, John Ristvey, Christine Morrow, and Michael Deal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teaching Nanoscience to High School Students Thomas R. Tretter . . . . . . . . . . . . . . Research on Pre-college Nanoscale Science, Engineering, and Technology Learning Lynn A. Bryan and Alejandra J. Magana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using Digital Tools in Nanoscience Education Laura Monica Gorghiu, Gabriel Gorghiu, and Ana Maria Aurelia Petrescu . . . . . . . . . . . . . . . . . . . . . . . . . . 3D Printing in the Context of Science, Technology, Engineering, and Mathematics Education at the College/University Level Peter Moeck, Paul DeStefano, Werner Kaminsky, and Trevor Snyder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dissemination, Outreach, and Training on Nanoscience and Nanotechnology Joaquin Tutor-S´anchez, David Quesada, Javier Gamo-Aranda, Noboru Takeuchi, Angela Camacho, Jordi Diaz, Fernanda Pilaquinga, Eliza Jara, Rainer Christoph, and Diana Padilla Rueda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diffusion of Nanotechnology Knowledge Using Mixed Methods Hamid Darvish . . . . . . A Scientometric Assessment on Growth of Nanobiotechnology Research Output R. Karpagam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Progress in the Development of a Systematic Nanoperiodic Framework for Unifying Nanoscience Donald A. Tomalia and Shiv N. Khanna . . . . . . . . . . . . . . . . . . . . . . Index

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Editor Klaus D. Sattler pursued his undergraduate and master’s courses at the University of Karlsruhe in Germany. He earned his PhD under the guidance of Professors G. Busch and H.C. Siegmann at the Swiss Federal Institute of Technology (ETH) in Zurich. For three years he was a Heisenberg fellow at the University of California, Berkeley, where he initiated the first studies with a scanning tunneling microscope of atomic clusters on surfaces. Dr. Sattler accepted a position as professor of physics at the University of Hawaii, Honolulu, in 1988. In 1994, his group produced the first

carbon nanocones. His current work focuses on novel nanomaterials and solar photocatalysis with nanoparticles for the purification of water. He is the editor of the sister references, Carbon Nanomaterials Sourcebook (2016) and Silicon Nanomaterials Sourcebook (2017), as well as Fundamentals of Picoscience (2014). Among his many other accomplishments, Dr. Sattler was awarded the prestigious Walter Schottky Prize from the German Physical Society in 1983. At the University of Hawaii, he teaches courses in general physics, solid-state physics, and quantum mechanics.

Contributors Biancamaria Baroli Dipartimento di Scienze della Vita e dell’Ambiente, Sezione Scienze del Farmaco Università di Cagliari Cagliari, Italy Ron Blonder Department of Science Teaching Weizmann Institute of Science Rehovot, Israel Lynn A. Bryan Department of Curriculum and Instruction and Department of Physics and Astronomy Purdue University West Lafayette, Indiana Angela Camacho Universidad de los Andes Bogota, Colombia Shalini Chaturvedi Department of Chemistry Sardar Patel University Gujarat, India Dong-Joon Cho Graduate School of Education University of Pennsylvania Philadelphia, Pennsylvania Nam-Joon Cho School of Materials Science and Engineering and School of Chemical and Biomedical Engineering Nanyang Technological University Singapore, Singapore Rainer Christoph Laboratorio de Nanotecnología Instituto de Ciencia, Tecnología e Innovación Universidad Francisco Gavidia San Salvador, El Salvador

Hamid Darvish Department of Information Management & Records Kastamonu University Kastamonu, Turkey

Laura Monica Gorghiu Department of Sciences and Advanced Technologies Valahia University of Targoviste Targoviste, Romania

Pragnesh N. Dave Department of Chemistry Sardar Patel University Gujarat, India

Gabriel Gorghiu Teacher Training Department Valahia University of Targoviste Targoviste, Romania

Michael Deal Stanford University Stanford, California

Gunnar Höst Department of Science and Technology (ITN) Linköping University Norrköping, Sweden

Paul DeStefano Nano-Crystallography Group Department of Physics Portland State University Portland, Oregon Jordi Diaz Universidad de Barcelona Catalunya, Spain Ugo Finardi CNR-IRCrES, National Research Council of Italy Research Institute on Sustainable Economic Growth Moncalieri, Italy Jennifer Flint Department of Science and Technology (ITN) Linköping University Norrköping, Sweden Viorel Nicolae Gaftea Research Institute for Artificial Intelligence “Mihai Drăgănescu” (ICIA) Romanian Academy Bucharest, Romania Javier Gamo-Aranda Division of Science & Engineering St. Louis University Madrid Campus Madrid, Spain

Douglas Huffman University of Kansas Lawrence, Kansas Jeremy S. Jackman Madda Educational Foundation, Inc. Bradenton, Florida and School of Education and Human Development Southern Methodist University Dallas, Texas Joshua A. Jackman School of Chemical Engineering Sungkyunkwan University Seoul, South Korea and Madda Educational Foundation, Inc. Bradenton, Florida Eliza Jara Pontificia Universidad Catolica del Ecuador Quito, Ecuador M. Gail Jones Alumni Distinguished Graduate Professor Department of STEM Education North Carolina State University Raleigh, North Carolina

x Anna-Leena Kähkönen Department of Teacher Education University of Jyväskylä Jyväskylän yliopisto, Finland Werner Kaminsky Department of Chemistry University of Washington Seattle, Washington R. Karpagam Ethiraj College for Women Chennai, India Shiv N. Khanna Department of Physics Virginia Commonwealth University Richmond, Virginia Frank Kupper Athena Institute STICHTING VU (VUMC) De Boelelaan, Amsterdam Alejandra J. Magana Department of Computer Information and Technology Purdue University West Lafayette, Indiana Ineke Malsch Malsch TechnoValuation Utrecht, The Netherlands Maria Francesca Matzeu Dipartimento di Scienze della Vita e dell’Ambiente, Sezione Scienze del Farmaco Università di Cagliari Cagliari, Italy Peter Moeck Nano-Crystallography Group Department of Physics Portland State University Portland, Oregon Christine Morrow University of Colorado Boulder, Colorado

Contributors Karljohan Lundin Palmerius Department of Science and Technology (ITN) Linköping University Norrköping, Sweden Ana Maria Aurelia Petrescu Teacher Training Department Valahia University of Targoviste Targoviste, Romania Fernanda Pilaquinga Pontificia Universidad Catolica del Ecuador Quito, Ecuador David Quesada Department of Mathematics Miami Dade College Wolfson Campus Miami, Florida John Ristvey UCAR Center for Science Education Boulder, Colorado Diana Padilla Rueda Facultad de Ciencias Basicas Universidad del Atlantico Puerto Colombia, Colombia Konrad Schönborn Department of Science and Technology (ITN) Linköping University Norrköping, Sweden Konrad Schönborn Linköping University Linköping, Sweden Carla Serri Dipartimento di Scienze della Vita e dell’Ambiente, Sezione Scienze del Farmaco Università di Cagliari Cagliari, Italy

Trevor Snyder 3D Systems Corporation Wilsonville, Oregon and Department of Mechanical and Materials Engineering Portland State University Portland, Oregon Aldrin E. Sweeney School of Education University of the West Indies Mona, Jamaica Noboru Takeuchi Centro de Nanociencia y Nanotecnologia, UNAM Ensenada, Mexico Donald A. Tomalia National Dendrimer & Nanotechnology Center NanoSynthons, LLC Mt Pleasant, Michigan and Department of Chemistry University of Pennsylvania Philadelphia, Pennsylvania and Department of Physics Virginia Commonwealth University Richmond, Virginia Thomas R. Tretter University of Louisville Louisville, Kentucky Joaquin Tutor-Sánchez Thermal Chain Technology Advanced Products Network Porto, Portugal and ETSI-ICAI Universidad Pontificia Comillas Madrid, Spain Ella Yonai Department of Science Teaching Weizmann Institute of Science Rehovot, Israel

1 Innovation and Entrepreneurship 1.1 Foreword: Scope, Organization and Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Introduction: Nanotechnologies and Their Relevance for Industrial Innovation and Entrepreneurial Activities; Rejuvenating Old Fields and Creating New Ones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Defining the Rules of the Game: What Is Innovation and Why It Is Important for the Success of Entrepreneurship? . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Nanotechnologies and Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1.5 Nanotechnologies and Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Before Innovation: Bibliometric Studies on Nanotech Scientific Production • Studies on Nanotech-Based Innovation: An Overview of On-Topic Scientific Literature in Management and Economics • Historical Path of Nanotechnological Innovation: Some of the Most Relevant Nanotech Innovations and of the Scientific Findings at Their Core • Case Studies of Nanotech Innovation in Scientific Literature

Ugo Finardi CNR-IRCrES, National Research Council of Italy, Research Institute on Sustainable Economic Growth

1.1

The Meaning of Innovation for Entrepreneurship: Why and How Firms and Entrepreneurs Innovate • Before Firms Innovate: Patenting in Nanotech. Literature Overview and World Patent Data • Industrial Outcome of Nanotechnologies: Industrial and Economic Data of Nanotech-Related Production • Case Studies of Relevant Successful Nanotech-Based Firms

1.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-12

Foreword: Scope, Organization and Target

The present chapter aims to offer the reader an overview of the role and importance of nanotechnologies and nanosciences for innovation, entrepreneurship and growth in the 21st century. Besides the scientific and technological achievements that have raised the attention on nanotechnologies in the last decades, it is also fundamental to highlight the present and future outcome of these achievements and their importance in fostering the invention, creation and production of novel goods and services, and in the improvement of existing ones. Innovation, a concept that will be further discussed in the following, benefits systematically of the outcome of research. In fact, the so-called “knowledge-based innovation” has a fundamental role, in particular, in the creation of novel radically innovative goods. Innovation, on its side, is fundamental for economic and social growth and, as such, has the indirect ability to improve life conditions, create workplaces and generate wealth. The present chapter is organized into five sections. The first one is a short introduction explaining the relevance and usefulness of nanotechnologies and nanosciences for

innovation and entrepreneurship. The second one is targeted at explaining in short the real nature and characteristics of innovation. The third and fourth sections are the core of the chapter, aiming at explaining in detail innovation and entrepreneurship in relation to nanotechnologies, using both theoretical descriptions and case studies. The fifth and final section presents some conclusions on the topic.

1.2

Introduction: Nanotechnologies and Their Relevance for Industrial Innovation and Entrepreneurial Activities; Rejuvenating Old Fields and Creating New Ones

Since the insurgence of nanotechnologies, it has been clear, to those involved in their studies and applications, that their future importance for creating new goods or services, or for rejuvenating old ones, would be relevant. This is due to some intrinsic features of nanotechnologies. The first of these features is with no doubt being transverse, and somewhat all-embracing, with respect to several scientific fields

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Public Policy, Education, and Global Trends

and technological application sectors. This is why nanotechnologies are sometimes defined as a “general-purpose technology” (GPT). GPTs are those technologies whose effect is both extensive and pervasive over a wide part of the society. These have been defined by Bresnahan and Trajtenberg (1995) as technologies presenting a potentially pervasive use in a broad range of sectors and technological dynamism. For instance, technologies belonging to this group are microelectronics at the end of the 20th century, or electricity and steam engines between the half of the 19th and the beginning of the 20th century. Some authors have recently assessed the characteristic of being a GPT for nanotechnologies. For instance, Mangematin and Walsh (2012) affirm that “Nanotechnologies are general-purpose technologies that act as both the basis for technology solutions across a range of industrial problems or as a nexus for the convergence of other enabling technologies” (p. 157). Such enabling technologies can be found in the range comprising physical and computational sciences, biotechnologies, as well as many social sciences such as communication technologies, cognitive sciences and social psychology. Also Kreuchauff and Teichert (2013) discuss the idea that nanotechnologies are a relevant GPT. So does Bhushan (2015) when he affirms that “Nanotechnology represents a ‘megatrend,’ and has become a ‘generalpurpose’ technology” (p. 1154). Besides being a GPT and also one of the NBIC (Nanotechnology, Biotechnology, Information technology, Cognitive science) technologies, nanotechnologies are a disruptive set of technologies (Finardi 2012). This is due to their ability to take over old market leader technologies once the market has assessed their characteristics. All these features of nanotechnologies mark deeply their relevance for industrial innovation and entrepreneurial activity in potentially any field of production and industry. The following sections will develop this topic, first discussing the general framework of innovation and industrialization, and then showing more in specific some of the most relevant features that nanotechnologies implement in various fields in order to enhance economy and growth.

1.3

Defining the Rules of the Game: What Is Innovation and Why It Is Important for the Success of Entrepreneurship?

It is a matter of fact that the term “innovation” is nowadays widely used, but often misused. We will try here to clarify its meaning, outlining and deepening the concept. We will also try to answer the question “where is innovation performed?”, and will also then categorize the different types of innovation. The answer to the question “what is innovation?” is easy as well as complex at the same time. First we must outline the historical path of the concept of “innovation”.

Austrian economist Joseph A. Schumpeter (1939)1 has been among the first ones to theorize it. Under this point of view his definition, taken from his book Business Cycles, still holds relevant and true nowadays: [. . . ] we include the introduction of new commodities which may even serve as the standard case. Technological change in the production of commodities already in use, the opening up of new markets or of new sources of supply, Taylorization of work, improved handling of material, the setting up of new business organization such as department stores – in short any “doing things differently” in the realm of economic life – all these are instances of what we shall refer by the term innovation. J. A. Schumpeter Business Cycles This definition, though dated, is still relevant and useful. Moreover, more recent ones (like the definition given by OECD that will be introduced later on in this work) are basically sketched out using this one as a blueprint. We must highlight some points of this definition. First, innovation has to do with any industrial production processes and is not just a matter of technology and research. Moreover, it is different from “invention”, which is purely technological or scientific while innovation may also not derive from an invention. Schumpeter (1928) affirms in fact that “Innovations [. . . ] alter the data of the static system and constitute, whether or not they have to do with ‘invention’ another body of facts and problem” (p. 366) and “It is quite immaterial whether this is done by making use of a new invention or not” (p. 378). He also describes the reasons why innovation is so important for economic growth (Schumpeter 1942). In his thought, innovation is a fundamental engine for the creation of profits. The two mechanisms of innovation are selection (competition between “innovative” and “traditional” firms) and imitation (possibility that “traditional” firms adopt innovations following “innovative” ones). Again the same author points out in his work the importance of the role of the entrepreneur for innovation. “Entrepreneur” is defined as the “new entrepreneur” entering the industries with new ideas, and thus new products, processes and organizational behaviors, or later as the “big incumbent”, setting high barriers to new entrants in an industry, and possessing large R&D facilities, which tend to accumulate knowledge.2 In all this path Schumpeter highlights

1 Joseph Alois Schumpeter was born in Tresch, Moravia, Austria-Hungary (now in Czech Republic), on February 8, 1883, and died in Taconic, Connecticut, United States January 8, 1950. 2 The two systems sketched here have been labeled by scholars studying the thought of Schumpeter respectively as “Schumpeter Mark I” (described in “The Theory of Economic Development: An inquiry into profits, capital, credit, interest and the business cycle”, 1911) and as “Schumpeter Mark II” (described in “Capitalism, Socialism and Democracy”, 1942).

Innovation and Entrepreneurship the central role of innovation in the dynamics of economy and of the growth of businesses, and the discontinuity in industrial change. Coming to the paths followed by innovative activities, these have been traditionally sketched with the socalled “linear model of innovation”, described synthetically in Figure 1.1. In this model innovation process starts from basic research (or, better, “target-free research”, or “research without immediate practical purpose”). The object of this activity is to discover the fundamental behavior of nature. Then “applied research” should be able to exploit fundaments of nature for practical purposes, that is, the results of basic research are transferred to applied research, then to development, and finally to production and diffusion. The transfer passages between the different steps are linear; there is no interconnection, and the flow of innovation moves in one direction – from the laboratory to the market. This model is considered nowadays only partial, not able to describe all the possible interactions existing in the “innovative process”, as well as for the “basic-applied” dichotomy (mainly due in this case to the complexity of contemporary research and to the need for accountability of researchers). Moreover, science and technology are often more and more intertwined, thus making even more difficult to distinguish the “nature” (under this point of view) of research activities. Coming to the different types of innovation, we still stick to Schumpeter’s definition. In his work “Theory of Economic Development”, Schumpeter (1911) defines five types of innovation: product innovation, process innovation, business model innovation, innovation in the source of supply, and innovation in mergers and diverters. In more recent times OECD has categorized innovation in a similar way. The “Oslo manual” (OECD 2005) in fact reports that “Four types of innovations are distinguished: product innovations, process innovations, marketing innovations and

1-3 organizational innovations. [. . . ] Product innovations and process innovations are closely related to the concept of technological product innovation and technological process innovation” (p. 47). Transversally to this classification we can also define “how deep” or “to what extent” we innovate. We can thus divide innovation into radical innovation and incremental innovation. A “radical innovation” is an innovation exploring new forms or technologies, is highly uncertain and risky, and possibly needs high knowledge inputs. On the other side, it has the power to disrupt markets and has higher chances of rewards. In practice we could approximate it as an innovation that entails the creation of novel disruptive products. “Incremental innovation” (which could be roughly defined as “improving the existing”) is on the contrary less risky and less rewarding. The division is obviously rough, and it is not always the case that an invention is “purely” radical or incremental. The complexity of the innovative process calls also for a redesign of its modeling. Kline and Rosenberg (1986) did theorize and outline a more coherent and realistic model, the “chain-linked” model, where all the steps of the innovative path are intertwined, and downstream steps are able to have an effect on upstream ones in order to fertilize and strengthen the process. This model, represented graphically in Figure 1.2, accounts for the structure of the relations in a “real” innovation process. Obviously this process is only sketched both in the theory and in the figure: a true innovation process can be even more complex, and, depending from case to case, interrelations between the parts can be many more. Moreover, the timing of when links are established should also be taken into account. The characteristics of the nature and the sketch of the complexity of innovation processes outlined above are intended as an introduction to frame the following part of the chapter. Now it is time to enter in medias res and to start discussing more in detail about nanotech innovation.

FIGURE 1.1

The linear model of innovation.

FIGURE 1.2

An example of an iterative, chain-linked model of innovation.

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1.4

Public Policy, Education, and Global Trends

Nanotechnologies and Innovation

Section 1.4.3 of this chapter discusses the first of its core topics: the relations existing between nanotechnologies and industrial innovation. The topic is tackled from different viewpoints, starting from the analysis of scientific literature on bibliometric and economic-managerial issues coming to the analysis of more specific case studies.

1.4.1

Before Innovation: Bibliometric Studies on Nanotech Scientific Production

Bibliometric studies are fundamental to understand where research is performed, how it has evolved and which research topics are mostly undertaken by scientists. They can also be a predictor of the fields that may have more relapses on innovation. This section presents a review of a selection of the most relevant on-topic literature published in recent years. The evolution of the scientific literature in social sciences on nanotechnologies and nanosciences until 2010 has been thoroughly reviewed by Huang et al. (2011). The study encompasses the review of more than 120 social science studies on the topic. Moreover, the authors discuss some relevant debates on nanotech and compare the strategies most used to harvest nanotech data. A parallel study is that of Motoyama and Eisler (2011) who measure nanotech scientific productivity combining bibliometric and funding data, exploiting dataset previously produced by other authors. The results show that the United States is not the leader in nanotech scientific productivity, being tackled by either European or Asian competitors depending on the exploited dataset. In the same year Grieneisen and Zhang (2011) undertake a study on the space and time distribution of nanotech scientific production. The results show an impressive growth of nanoscale studies since the end of the 1990s. Moreover, the authors argue that “while the 1–100 nm criterion is convenient, it is too simplistic to reflect either the scientific reality of size-dependent characteristics among all materials or the general usage of these terms” (p. 2838). Also Coccia et al. (2012) analyze the time and space trajectories of nanotechnologies, showing the growth in life science applications, as well as the polarization around North America and Europe and the growing role of China and South Korea. A different topic is instead tackled by Didegah and Thelwall (2013) who deepen the causes and effects of citations on nanotech literature. Their findings show that those articles that cite high-impact works tend to be more cited than others. Also the number of institutions collaborating to the paper has an effect on the growth of received citations. In more general terms, “the impact of the publishing journal and references are the main extrinsic factors behind the citation impact of individual papers in nanoscience and nanotechnology” (p. 1063).

A rather original topic is instead discussed by Arora et al. (2014) who measure how a common scientific lexicon has developed in nanotech. The study encompasses a time span of 21 years, examining how a set of “nano-” prefixed terms grows in a set of publications. The evolution shows a progressive differentiation of terms as well as a growth in the number of papers based on biomedical studies and clinical applications of nanotechnologies. The same group of authors did present a thorough search strategy set to identify scientific output in nanotech (Arora et al. 2013). The methodology identifies a set of queries that, combined, allow obtaining a comprehensive dataset of nanotech-based scientific publications. Bartol and Stopar (2015) perform a study based on a more limited database trying to disentangle how “nanoconcepts” are distributed among main nanoscience journals. The most interesting finding is probably the fact that such “nanoconcepts” are distributed according to a power law, in particular Zipf’s law, and the relationship is almost linear on logarithmic scale. Muñoz-Écija et al. (2017) exploit instead a relevant database of publications retrieved on Web of Science. Their aim is to disentangle “the intellectual and cognitive structure of nanoscience and nanotechnology” (p. 62) using visualization techniques. The methodology allows to identify seven groups within the whole intellectual structure of nanoscience and nanotechnology. Each group makes up to a seminal paper of the field. A relevant number of scientific works deal with scientometric studies related to specific nanotech topics or issues. Finardi (2011) disentangles the time relations existing in nanotech between the publication of a scientific article and its citation into a patent. The aim is to measure how fast scientific knowledge turns into an innovation. The results show that the most representative time lag is centered around 3–4 years. The topic is further discussed in Finardi (2013) with the support of a deeper theoretical framework. The findings on time and space relations between publication and patenting support – and can be framed in – the theory of technological paradigms described in Dosi (1982). Some specific topics, among others, that have undergone research activities in the recent past are biomedicine (Coccia and Finardi 2012), non-thermal plasma in medicine (Coccia and Finardi 2013), nanotechnology applied in oncology (Dong et al. 2013), nanocatalysis (Zibareva et al. 2014), nano-energy (Guan and Liu 2014), nano-enabled drug delivery for brain cancer (Huang et al. 2015) and carbon nanotubes (Pellegrino et al. 2016). This selection shows that the interest of bibliometric studies on nanotechnologies and nanosciences is wide and goes deeper than the general analysis of the field. Besides the study of specific nanotech topics, other issues have also attracted the interest of scholars in the field of bibliometrics and scientometrics. Sotudeh and Khoshian (2014) deepen the relations between gender and web presence in nanotechnologies. They find out that female and male web-present nanoscientists are equal in terms of scientific productivity, while they are not higher in

Innovation and Entrepreneurship citations with respect to web-absent female researchers than their male counterparts. Differences between developed and developing countries are instead the topic of the work of Jafari and Zarghami (2016). A more specific subject – Russia’s nanotechnology – is studied by Terekhov (2017). Finally, a relevant perspective is that of Suominen et al. (2016). Their aim is to study the transition of nanotechnologies toward their next generation. In particular, these will be active nanotechnologies, able to incorporate functions responding to the surrounding environment, or incorporating dynamic functions capable to change their state while the device is in use. Their complex bibliometric methodology shows “increasingly higher growth in the rate of active nanotechnology publications and a relative increase in the share of active nanotechnology research among all nanotechnology publications. This suggests that there is beginning to be a shift in focus from passive to active nanotechnology research” (p. 14).

1.4.2

Studies on Nanotech-Based Innovation: An Overview of On-Topic Scientific Literature in Management and Economics

Since the insurgence of the “nanotech era”, scholars in social sciences have paid attention to nanotech-enabled innovations and have thus deepened the economic and managerial effects of nanotechnologies and nanosciences. This section will review a selection of recent literature discussing this topic. Some studies support the idea that the next Kondratieff wave (named after the Russian economist N.D. Kondratieff) will be driven by nanotechnologies, or that nanotechnologies will have a fundamental role in its insurgence (Wonglimpiyarat 2005; Grinin et al. 2017).3 In particular, according to Grinin et al. (2017), the main contribution of nanotechnologies will be in medical technologies, combined with many other technologies. The above-cited work of Mangematin and Walsh (2012) did briefly discuss the evolution of nanotechnologies toward becoming general-purpose technologies. Yet in 2012 products incorporating nanotech innovations were on the market. Thus the authors affirm that the “pan-industry nature” (p. 158) of nanotechnologies, besides the production and adoption of specific nanoproducts (materials and components or full systems and devices), resides also in the abilities to perform a radical change of industries. Moreover, they have been able to create new sectors like nanobiotechnologies. The same authors introduce in this work what they call the “paradox of nanotechnologies”: “nanotechnologies are found everywhere except as a new industry or a new scientific field” (p. 159). A similar perspective is that of Roco et al. (2011) who examine the progress made from 2000 onwards in the

3 Kondratieff waves are long waves of economic cycles, mainly theorized in his work “The long waves in economic life”.

1-5 development of nanotechnology, as well as the opportunities for the years up to 2020. In the conclusions, the authors affirm that “There is a need for continued, focused investment in theory, investigation methods, and innovation at the nanoscale [. . . ] more traditional industries may provide opportunities for large scale application of nanotechnology [. . . ] Public–private partnerships need to be extended in research and education” (pp. 909–910, passim). Regarding this last statement it is relevant to cite the work of Chen et al. (2013). The authors of this work exploit a mixed database of publications (from ISI-WoS) and patents [from United States Patent and Trademark Office (USPTO)] to assess the evolution of the societal and economic benefits of nanotechnologies in a 20-year longitudinal analysis. Moreover, they highlight the relevant role of public funding initiatives, such as the National Nanotechnology Initiative in the United States. Patents and publications supported by National Science Foundation (NSF) public funding for fundamental research received a higher number of citations. The topic of technology and knowledge transfer in nanotechnologies is also discussed in several works. Zalewska-Kurek et al. (2018) exploit an empirical study performed in a nanotechnology institute to support a theory of strategic positioning of the behavior of scientists in knowledge transfer. The results show that “to increase the likelihood of scientists engaging in knowledge transfer to industry, scientists need to have a high need for autonomy [. . . ] and a high need for interdependence” (p. 139). Technology transfer in a specific field – nanowires – is instead the concern of Ozcan and Islam (2014) who analyze patent data to establish networks and to find out that “networks or clusters for nanowire technology [. . . ] vary greatly from one country to another” (p. 129). An in-depth analysis of the model of technology transfer in nanotechnologies is the issue tackled by Genet et al. (2012). In particular, they focus on the role of smalland medium-sized enterprises (SMEs) in the production and transfer of knowledge and comparing nanotech with biotech and microelectronics. The results show that the model of nanotechnologies “is closer to that of microelectronics, where large firms establish direct linkages with public research organizations. SMEs do not play the same bridging role in the nanotech model that they did in biotech, but rather act as providers of specialized services and technologies” (p. 214). Research and innovation in nanotechnologies benefit much from dedicated large infrastructures. The Grenoble area stands in Europe as the host of what are probably the hugest facilities dedicated to nanotechnologies. The study of Finardi (2013b) presents the first evolution of this infrastructure, MINATEC, a public-owned center combining university education, laboratories for applied research, and facilities for enterprise incubation. In this case, physical clustering is the way followed to obtain operational clustering in a sort of “virtuous circle”. More recently, clustering of activities has expanded in the Grenoble Isère Alpes

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Nanotechnologies (GIANT), an even larger infrastructure. Scaringella and Chanaron (2016) study its socioeconomic and entrepreneurial effects, and conclude that the (huge) investments made in such infrastructures (€2.8 billions) are worthwhile by both economic and social points of view. The social impact of nanotech research is also measured on an American sample (that of US NSF initiatives) by Bozeman and Youtie (2017). In particular, the work is articulated over four case studies of three programs and one administrative policy initiative. The authors identify gaps undermining the ability to assess socioeconomic benefits of research. Another American case study is that proposed by Youtie and Shapira (2017) who focus on the “Innovation corps” program, started in 2011 by NSF to speed up commercialization of science-driven discoveries, and more specifically in the case of nanotechnology commercialization. The paper argues that public values are compatible with economic development program values. The authors end up suggesting that “Commercialization should be conceived not as antagonistic to public values, but rather as a mechanisms for the incorporation, co-existence and balancing of values” (p. 1373). The topics highlighted in this section are, obviously, only a meaningful selection of the most relevant ones related to innovation with a specific topic on nanotechnologies. Nevertheless the aim is also to encourage the curiosity of the reader toward these topics and serve as a starting point for further research related to innovation in nanotechnologies.

1.4.3

Historical Path of Nanotechnological Innovation: Some of the Most Relevant Nanotech Innovations and of the Scientific Findings at Their Core

Nanosciences and nanotechnologies are relatively young disciplines, and that their historical path spans only some decades. Nevertheless it is yet possible to outline the most relevant facts or achievements in nanotech that can be considered landmarks and milestones. This section presents a reasoned list of some of these facts. The list is obviously neither exhaustive nor fully descriptive of nanotech history, but it clearly shows a trend going from theorization to the first inventions and scientific discoveries [Scanning Tunneling Microscope (STM) and Atomic Force Microscope (AFM)], buckminsterfullerene and nanotubes) to the practical applications in everyday life. Some earlier achievements account for the universal nature of nanotechnologies.

Years Ca. 300

1857

Achievements and Facts Creation of the Lycurgus cup. This artifact is built using dichroic glass, which is able to show different colors depending on illumination. This glass was made dispersing gold and silver nanoparticles into the glass itself. Michael Faraday discovers colloidal gold.

Years 1959

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1985 1986 1986 1989

1990 1991 1992

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1.4.4

Achievements and Facts On December 29, 1959, Richard R. Feynman (who is to date the youngest Nobel Prize Laureate for Physics ever) gives a talk at an American Physical Society meeting at California Institute of Technology. The talk describes the chances of modifying matter with atomic precision and contains the famous sentence “there is plenty of room at the bottom”. The term “nano technology” is used for the first time by Japanese scientist Norio Taniguchi of Tokyo Science University, who writes it in an article on ion-sputtering machining. Gerd Binnig and Heinrich Rohrer invent STM at IBM labs in Zürich. The Russian Physicist Alexei Ekimov discovers quantum dots and conducts studies on their electronic and optical properties. Harold Kroto, Robert Curl and Richard Smalley discover Buckminsterfullerene (Buckyball). Gerd Binnig, Calvin Quate and Christof Gerber invent AFM. Gerd Binnig and Heinrich Rohrer receive Nobel Prize in Physics for the invention of STM. Donald M. Eigler and Dr. Erhard K. Schweizer of the IBM. Almaden Research Center at San Jose, California, spell “I.B.M.” with 14 Xenon atoms on a nickel surface using an AFM. In this year the first journal on the topic, Nanotechnology, starts publication. Japanese scientist Sumio Iijima discovers carbon nanotubes at NEC Corporation. K.E. Drexler publishes what is probably the first textbook on nanotechnologies, Nanosystems: Molecular Machinery, Manufacturing and Computation (Drexler 1992). Harold Kroto, Robert Curl and Richard Smalley receive the Nobel Prize in Chemistry for the discovery of Buckminsterfullerene. Dr. Nadrian C. Seeman and his co-workers at New York University announce the building of the first DNAbased nanomechanical device. On January 21, President W.J. Clinton unveils the American National Nanotechnology Initiative in a science policy address at California Institute of Technology. Konstantin Novoselov and Andre Geim isolate graphene from bulk graphite and observe its features after transferring it on a silicon wafer. The Kavli Prize in Nanoscience is established by the Norwegian Academy of Science and Letters, the Norwegian Ministry of Education and Research, and The Kavli Foundation together with the prizes in Astrophysics and Neuroscience. Konstantin Novoselov and Andre Geim receive 2010 Nobel Prize in Physics thanks to their pioneering studies on graphene.

Case Studies of Nanotech Innovation in Scientific Literature

In order to show the multifaceted versatility of nanotechnologies, some case studies will be described starting from the analysis of scientific literature, in particular, of recent review articles. In this way it is possible to show what nanotechnologies do for innovation in applied research. The first relevant case to be described is that relative to the use of nanoscale delivery systems for targeted chemotherapy in the cure of cancers (Xin et al. 2016). Examples of nanocarriers used in chemotherapy are liposomes, nanoparticles and polymeric micelles. Such nanocarriers bear the advantage of an enhanced permeability and retention effect. This effect enables them to accumulate in tumors at a much higher concentration than they do in normal tissues. Moreover

Innovation and Entrepreneurship they can be designed with size and surface characteristics able to increase the time in circulation and biodistribution. These can regulate biodistribution and pharmacokinetic properties of the drugs carried. This can help overcome the problems due to multidrug resistance. Moreover other strategies in anti-cancer therapy involving nanocarriers are studied, such as immune, gene and vascular targeting therapies. Immunotherapy exploits the immune system of the same patient in the fight against cancer. Delivering immunotherapy via nanocarriers might represent a strategy to improve its efficiency, enhancing the recognition and destruction of cancer cells. Vascular therapy, on its side, is targeted at either preventing the formation of new vessels or destroying the cancer’s existing vascular system. Again nanocarriers are extremely promising to this end. Gene therapy, which involves the use of therapeutic genes, can also benefit from the use of nanocarriers. In summary, “efficient drug delivery systems that can achieve both targeted and controlled release of chemotherapeutic agents in tumor and/or endothelial cells can be successfully constructed with a desirable size distribution and high encapsulation efficiency [. . . ] targeted drug delivery [. . . ] can improve the effectiveness of chemotherapy agents” (Xin et al. 2016, p. 29, passim). A second case deserving attention in the field of nanostructured materials is that of polymer-based nanocomposite materials. Composite materials are made of a reinforcement (a nanostructured material in the present case) embedded in a matrix (a polymer in the present case). Such materials are a rather vast category and may have a number of applications. Polyurethane-based nanocomposites can be enhanced, with respect to the sole polymer, in terms of their mechanical, electrical, thermal, acoustic, chemical, shape memory, and viscoelastic properties (Vaithylingam et al. 2017). A more specific case is that of carbon nanotubes– enhanced polyurethane nanocomposites. The features of carbon nanotubes can radically enhance the properties of the polyurethane, increasing features such as mechanical (hardness, compressive, flexural and tensile strength, modulus, stiffness, toughness) and thermal (conductivity and stability) ones. Acoustic properties can be enhanced incorporating nanoclays, titania nanoparticles or multiwalled carbon nanotubes. A relevant property is that of shape memory. This is the ability to change shape depending on the exposure of an external stimulus, changing from a shape programmed temporarily to an original permanent one. To this end the pathway is to introduce polylactide in the polyurethane matrix. Such composites “can be used for applications like hydrogen storage, super capacitors, biosensors, electromechanical actuators/resonators, nanoprobes for high-resolution imaging, solar cells, aerospace shuttle parts, aeronautical parts, photovoltaic devices, cardiac assist pumps, blood bags, coatings, wind turbine blades, body armor, car parts, yacht structures, semiconductor, and sports equipment” (Vaithylingam et al. 2017, p. 1538). The third and last case, in this field, is that of biomimetic materials for osteointegration. A case of a new biomimetic

1-7 material for bone regeneration has been described in Finardi and Sprio (2012). A bone-substitution material was obtained via pyrolysis of wood, which did then undergo carburization, oxidation, carbonation and phosphation. This innovation opens up the discussion to the use of nanostructure hydroxyapatite (HA) for regenerative medicine (Tampieri et al. 2016; Ballardini et al. 2018; Russo et al. 2018). The aim of such materials is to create “threedimensional (3D) constructs that are able to exchange chemical signals promoting osteogenesis and can then be progressively resorbed during the formation and remodeling of new tissue [. . . ] osteoconducting structures that have the ability to host human cells” (Tampieri et al. 2016, pp. 120–121, passim). Once synthesized, HA nanocrystals can be used to create biomimetic materials. Moreover, bioactive iron-substituted apatite presents superparamagnetism and can thus be exploited in order to create new biomedical devices possessing functionalities that can be switched on or off via exposure to magnetic fields.

1.5

Nanotechnologies and Entrepreneurship

The fourth section of this chapter presents the second of its core topics, as it discusses the benefits of the use of nanotechnology innovations in the entrepreneurial environment. First of all the reasons standing behind industrial innovation are discussed. Then the topic of patenting – a fundamental instrument for entrepreneurs to innovate – is introduced, with an obvious specific focus on nanotechnologies. Finally data on nanotech industries and firms, and some specific case studies, are introduced.

1.5.1

The Meaning of Innovation for Entrepreneurship: Why and How Firms and Entrepreneurs Innovate

Innovation has been defined often as one of the main engines of entrepreneurship. This is mainly due to the fact that innovation can be one of the essential tools firms can exploit in order to achieve success. As one may obviously think the importance of innovation is due to its role in the situation in which firm and entrepreneur want to respond to a need from the market and thus try to answer to this need with a solution. The solution is a new product or a new service, or an improvement of existing ones, which – besides responding to a need – is a way to increase business. Innovation is how an entrepreneur can reach this target, in order to keep his business at pace with competitors in terms of products or services, of their quality and of their price. Again Joseph Schumpeter helps us understand why firms should innovate: The innovation is hazardous, impossible for most producers. But if someone establishes a business having regard to this source of supply [. . . ] then he can produce a unit of product more cheaply, while

1-8

Public Policy, Education, and Global Trends at first the existing prices substantially continue to exist. He then makes a profit. [. . . ] And again the latter [. . . ] perish in the vortex of the competition which streams after them. The case of the choice of new trade routes belongs here. J.A. Schumpeter The Theory of Economic Development (1911)

According to Dodgson (2017), “The firm is the central mechanism for converting innovation into economic action. [. . . ] whether motivated by profit, growth, or simple survival, innovation is a primary activity for firms as they attempt to meet their objectives” (pp. 85–86, passim). He lists several processes (among them research and technology led, internal coupling, external collaboration and strategic integration) and practices (such as positioning as leader or follower, connecting vertically or horizontally, protecting and organizing) firms may use to innovate in their specific context depending on dimensions and sector. It must be noted at this point that technological innovation is without doubt the most relevant type of innovation that matters when considering nanotechnologies. In this case internal R&D is not the only instrument that firms and entrepreneurs can exploit in order to innovate. In fact another means of innovation offering chances to perform innovation is the so-called “technology transfer”. This term encompasses the activities performed by universities and public research bodies besides the two main tasks of research and teaching. This involves also all the activities leading to a commercial exploitation of the “academic knowledge”, supporting research and contributing to the economic development of a region or a country. The most relevant ones among such activities are the creation of academic spinoffs and academic patenting. In order to allow entrepreneurs to access “academic knowledge” research bodies set up dedicated offices. Such offices have usually the double function of performing technology scouting internally and to sell and commercialize knowledge externally. Besides technology transfer offices, many universities create “enterprise incubators”, that is, structures offering to newly created businesses facilities and services (consultancy, legal advice, etc.) as well as access to funding and partnering. The aim is obviously to increase the chances of survival for the new company, which is in most cases deriving from the same university or institution. Finally a relevant concept we have to introduce when discussing innovation in a firm is that of “open innovation”. The concept is relatively recent, and we owe its definition to H.W. Chesbrough: Open Innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology. H.W. Chesbrough et al., Open Innovation: A New Paradigm for Understanding Industrial Innovation (2006)

Thus a regime of “open innovation” allows the use of external ideas, allowing some “free flow” of knowledge among firms. This has some drawbacks as it entails the spillover of internal knowledge to external actors (mainly other firms of the same geographic area and/or industrial sector). On the one hand, gains and losses must be carefully evaluated in the balance of knowledge flows, and on the other, there are often chances that the right answer to innovation needs may derive from outside the firm’s boundaries. This minimal synthesis of the paths and instruments that can be exploited by firms and entrepreneurs to perform innovative activities is obviously meant as a simple introduction to the topic. Due to its relevance it deserves being introduced prior to tackle more directly the topics relative to innovation in entrepreneurship.

1.5.2

Before Firms Innovate: Patenting in Nanotech. Literature Overview and World Patent Data

Patenting is one of the basic activities of industrial exploitation of innovation. This is even truer in hi-tech industries, such as many of those related to nanotechnologies. Due to this fact we provide here a short overview of the studies related to patenting in nanotechnologies, corroborated with some citations of data on world scientific nanotech patenting. It must be made clear that patenting an invention in order to exploit Intellectual Property Rights out of it is often basic for growing a business but has some implications that make it a somewhat complex task. First of all, in order to obtain a patent, the invention must be disclosed, as the content of a patent must be made available. Then the invention must respect some requirements in order to obtain a patent. It must be novel (no prior art must exist under any form), inventive activity must be demonstrated, it must show the possibility to be industrialized, it must be sufficiently described in the patent (the form of the description must allow replicability) and it must obviously be legal. It is important to remember that the International Patent Classification (IPC) system and the Cooperative Patent Classification (CPC) scheme contain a class dedicated to nanotechnologies, class B82Y.4 We start our review of literature citing a recent work of Zhu et al. (2017) studying patents (together with papers and NSF awards) in order to present the development of nanotechnologies in more recent years (2010–2016). Patent data are collected from USPTO and WIPO (World Intellectual Property Organisation). USPTO data show a steady growth from 2000 onwards, and, in specific, a steeper growth after 2009. The fraction of US patents over the total diminishes over time. WIPO nanotech patents increased steadily from around 2,000 in 2000 to over 42,000 in 2015. In this

4 A useful description of patent classification can be found in Jürgens and Herrero-Solana (2017).

Innovation and Entrepreneurship period, the main share was that of China, with almost two-thirds (62.78%) of patents awarded. Similar results were yet described by Chen et al. (2008, 2013). These studies are somewhat complementary to that of Jürgens and Herrero-Solana (2017). The authors analyze nanotechnology patent classification in different patent systems. They conclude that, besides the fact that keyword search is not a suitable way to perform a comprehensive patent search in nanotechnology, there are also relevant differences between different patent classification systems in terms of the number of patents. In particular, “Using the B82Y symbol in the Cooperative Patent Classification retrieves far more patents than using the International Patent Classification and thus is recommended for use for a nanotechnology patent search” (p. 151). The former search rendered in fact more than 30,000 patents versus the 40,000 found via the latter. Another work based on patent data and complementary to those above reviewed is that of Ozcan and Islam (2017). These authors use a mixed methodology to build their database, combining nano-related terms with the abovedescribed search in patent class B82. In this way they gather almost 50,000 patents to draw their analysis on. Almost 30,000 of such patents are owned by corporations, while another 10,700 by academic organizations. In several cases patents are co-owned by a research institution and a corporation, and some strong links in this sense – in particular in Asian countries – are established. Nevertheless, as the

1-9 authors affirm, “Strengthening the linkages between scientific and corporate actors may eliminate many barriers and accelerate the diffusion of technology in the commercial and scientific fields” (p. 966). While in China, South Korea and Japan intra-national connections between important corporations and research centers are strong, patent data show also relevant collaborations between French and American institutes and firms. International collaboration is also the key element of the article of Zheng et al. (2014). These authors gather USPTO patents up to 2010 in order to provide an overview of the development of nanotechnologies and, what is more relevant, of international collaboration. Data show that patents springing out of international collaborations are a minority (>10%) over all the time series (from 1991 onward). Moreover the trend of the share of international collaboration patents is fragmented and difficult to interpret. Switzerland shows the highest rate of international collaboration patents (63.5%), while other countries present a value above 40% (the United Kingdom, The Netherlands, Belgium, Sweden, Russia, India and Singapore). In absolute terms the most important player in terms of international collaboration nanotechnology patents is the United States. Summing up, patent data show a fast rise of activities and a turbulent growth. In order to add evidence and to further highlight how patenting in nanotechnologies has evolved, we use OECD data on the share of the different economies in nanotechnology-related patents. The graph in Figure 1.3

FIGURE 1.3 Economies’ share in nanotechnology-related patents, OECD countries, 2000–2014. (Source: OECD Key Nanotech Indicators, www.oecd.org/sti/nanotechnology-indicators.htm, visited November 2018.)

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Public Policy, Education, and Global Trends

shows, in partial accordance with the results of Zhu et al. (2017), that the share of US patents decreases over time. A similar trend is presented by Japan, while EU-28’s share is more stable (but nevertheless decreasing in the last years). China sees a deep rise of its share in 2014 only (more recent years are not encompassed in OECD dataset).

1.5.3

Industrial Outcome of Nanotechnologies: Industrial and Economic Data of Nanotech-Related Production

In terms of innovation activities it is obvious that, as for any other technological field, the natural output of nanotechnologies is in industrial production with their incorporation in goods of any kind. This industrial output, and the incorporations of nanotechnological findings that make it possible, is made somewhat easier by the fact that nanotechnologies are generally considered disruptive and generalpurpose technologies as described by Finardi (2012). On the other side, these features of nanotechnologies make it much more difficult to gather information on industrial production involving in some way nanotechnologies. Thus, also in scientific literature examples of this kind (that is, scientific works collecting and reporting data on nanotech industrial production) are very scarce. An earlier work on this topic is the paper of Piccinno et al. (2012). These authors perform a survey to obtain information, and then report the estimates (both at European level and at worldwide) for the production and use of the different engineered nanomaterials: TiO2 , ZnO, FeOx , AlOx , SiO2 , CeO2 , Ag, quantum dots, carbon nanotubes and fullerenes. The survey is performed through online interviews to a number of experts (mostly manufacturers and downstream users). According to the data gathered from the survey, the median value of the most produced nanomaterial, at the time SiO2 , was 5,500 tons/year worldwide. The second one in this ranking was TiO2 , with 3,000 tons/year worldwide. The other nanomaterials follow at distance (from the 550 tons of ZnO to the 0.6 of fullerenes and quantum dots). All these data suffer from a high variance. The survey analyzed also the distribution of nanomaterials in final products, but in this case data are more disperse and less reliable. Another earlier contribution, that of Hendren et al. (2011), estimates data for the production of five nanomaterials in the United States. Also in this case data are dispersed, and nano-TiO2 is the nanomaterial presenting the highest production, with values ranging from 7,800 to 38,000 tons/year. Carbon nanotubes range between 55 and 1,101 tons/year. In order to gather data on nanotech industrial production, a reliable source is the OECD and its database for nanotechnologies.5 The database does not report, perhaps for the above-presented reasons, data on industrial

5 www.oecd.org/sti/nano/ and subpages therein; www.oecd. org/sti/nanotechnology-indicators.htm (links visited March 2018).

production; nevertheless, as Figure 1.4 shows, it presents data on the number of firms active in nanotechnologies for some of the countries that take part in the organization. The two series of data are relative to firms employing nanotech and to firms that devote the majority (>75%) of their production to nanotech. In the first group the United States first, and then Germany and France, overcome largely the other countries. But, in the second group it is Germany and France that present the highest number of firms. This second set of data on the number of firms can be coupled and read together with another set of data, that of nanotech R&D as a percentage of BERD (Business Expenditure in R&D) for nanotech R&D firms reported in Figure 1.5. In this case it is the United States that presents the highest value, with a fraction of R&D expenditure dedicated to nanotechnologies over 5%. Among the other OECD countries encompassed in the database only the Russian Federation is above 3%. Thus the United States is the country presenting the highest R&D intensity in the field.

1.5.4

Case Studies of Relevant Successful Nanotech-Based Firms

The present section will present some relevant case studies of nanotech-based firms, drawing information from scientific literature, from the firms’ websites and from nanotech-specific websites. Information will be anonymized for the sake of privacy issues. The first case is that of a small Spanish-based company. Its core business is the production and sale of graphene. The company started its business in 2010 and has its main branch in the Basque Country in Spain, as well as a subsidiary in the United States. It exports graphene to 60 countries, to both universities and research centers, and businesses. The company has received financing from a major oil and gas company to boost its business. Besides being involved in commercial activities, the company performs obviously research activities and has published scientific works in major nanomaterials-related journals. The company in fact has developed specific graphene production technologies, exploited in its own production plant. The second case is a US company active in the field of bionanotechnology. The company has its main branch in California and was founded in 2003; it belongs at present to a group of medium enterprises. The core business of the company is in genome biology. The fields of application range from cancers and human disease, agricultural bioengineering to discovery of genomes. In more specific, the company produces genome mapping and analysis tools; it has developed a platform that is able to provide single-molecule analysis of long DNA chains, as well as other bionanotech solutions. Again the firm associates research and publication in valuable journals to production and commercial activities. Another California-based company is the subject of the third case. Founded in 2001 by a university professor,

Innovation and Entrepreneurship

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FIGURE 1.4 Number of firms active in nanotechnologies, last available year. Note: Nanotechnology firms are those that use this technology to produce goods or services and/or perform R&D. Dedicated nanotechnology firms are those devoting at least 75% of their production of goods and services, or R&D, to nanotechnology. Data for all countries (but for Germany, Mexico and Poland) are those relative to R&D nanotechnology firms. Last available year is either 2013, 2014 or 2015. Data for Brazil are relative to firms with 10 or more employees only. In the case of Canada, counts of nanotech R&D firms from the R&D survey will not be possible after 2013 due to a methodology change. Data for Denmark are preliminary. For Germany data are relative to those firms having nanotechnology as a part of the core business and accounting for 60% or more of the business activity. Data for Japan are relative to enterprises with a paid-in capital of 100 million yen or more. Data for Mexico are relative to firms with 20 or more employees only. Data for the Russian Federation exclude small business enterprises performing nanotechnology R&D. Statistics for the number of companies in the United States are based only on companies that reported to the survey, do not include an adjustment to the weight to account for unit nonresponse, and are relative to firms with five or more employees only. (Source: OECD Key Nanotech Indicators, www.oecd.org/sti/nanotechnology-indicators.htm, visited November 2018.)

its business was based on a patented catalytic technology to produce single-walled carbon nanotubes (SWNTs). The patented method allowed to produce high-purity SWNTs, and the company’s products were distinguished for this characteristics. Moreover a further refinement of the process allowed high productivity coupled with selectivity. Notwithstanding the fact that the management was not able to raise fund to continue operations, the technology of the company proved itself successful as it has been acquired together with physical assets by a bigger company that continues operating the SWNTs production plant. The above-described case studies are obviously only illustrative of the possibilities offered by industrialization of nanotechnologies. They may serve both as a material for further investigation and as an example to offer to those interested in the field.

1.6

Concluding Remarks

The aim of this chapter was to offer a synthetic but complete overview of the importance and meaningfulness of nanotechnologies and nanosciences for innovation, entrepreneurship and growth. While it does not enter into the complex and somewhat vague exercise of devising its future, it offers a recollection of recent features and achievements in this field. Thus it tries to describe – mostly keeping an eye on the scientific literature discussing the topic – the innovative nanotech achievements and their evolution, as well as their relapses on entrepreneurship and economic growth. The historical path of nanotechnologies and nanosciences is relatively short, as it actually encompasses few decades over the 20th and 21st centuries. Nevertheless, as also the data reported in the present chapter show, it is possible

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Public Policy, Education, and Global Trends

FIGURE 1.5 Nanotech R&D as a percentage of BERD for nanotech R&D firms, last available year. (Source: OECD Key Nanotech Indicators, www.oecd.org/sti/nanotechnology-indicators.htm, visited November 2018.)

to follow an evolution of both nanotech findings in scientific research and nanotech-based innovations performed in entrepreneurship. Under this point of view a bird’s-eye view of managerial-economics scientific literature seems to show that the initial hype set on nanotechnologies could have slightly waned in the last years. This fact, if confirmed by a bibliometric quantitative analysis, might have a simple explanation. Nanotechnologies are more and more incorporated in goods, either new or improved ones. Thus we might be slowly moving from a situation where the use of nanotechnologies was novel and – we might say – at the edge of innovation to a new situation where nanotechnologies are a common instrument for innovation in entrepreneurship. This could be interpreted, according to what discussed above on the nature of innovation, as “more incremental innovation, less radical innovation”. This statement is only partly true. In fact, while incremental innovation is often considered as less difficult and within everyone’s reach, it is not always the case that technologies in a more advanced status of evolution offer incremental innovation only to the path of industrial evolution. Instead, as just discussed, it may simply be the case that radical nanotech innovations are (becoming) less striking and sensational, and are able to enhance technological growth without being constantly under the spot. As a consequence this means, on the one hand, that nanotechnologies (and in parallel nanosciences) have become more straightforward and thus a less appealing

topic for those tackling innovation studies but, on the other, confirms the great relevance nanotech has for growth and, thus, the importance of scientific and technological research targeted at its evolution and, in many cases, at its applicative outcome. This relevance, witnessed also by the content of the present chapter, makes nanotechnologies and nanosciences a topic that – besides the trends of the interest of researchers – will undoubtedly continue being studied also in social sciences in the years and decades to come.

References Arora S.K., Porter A.L., Youtie J., Shapira P. (2013). Capturing new developments in an emerging technology: An updated search strategy for identifying nanotechnology research outputs. Scientometrics. 95(1), 351–370. Arora S.K., Youtie J., Carley S., Porter A.L., Shapira P. (2014). Measuring the development of a common scientific lexicon in nanotechnology. Journal of Nanoparticle Research. 16, 2194. Ballardini A., Montesi M., Panseri S., Vandini A., Balboni P.G., Tampieri A., Sprio S. (2018). New hydroxyapatite nanophases with enhanced osteogenic and anti-bacterial activity. Journal of Biomedical Materials Research. A106A(2), 521–530.

Innovation and Entrepreneurship Bartol T., Stopar K. (2015). Nano language and distribution of article title terms according to power laws. Scientometrics. 103(2), 435–451. Bozeman B., Youtie J. (2017). Socio-economic impacts and public value of government-funded research: Lessons from four US National Science Foundation initiatives. Research Policy. 46(8), 1387–1398. Bresnahan T.F., Trajtenberg M. (1995). General purpose technologies ‘engines of growth’ ? Journal of Econometrics. 65(1), 83–108. Bhushan B. (2015). Governance, policy, and legislation of nanotechnology: A perspective. Microsystem Technologies. 21(5), 1137–1155. Chen H., Roco M.C., Li X., Lin Y. (2008). Trends in nanotechnology patents. Nature Nanotechnology. 3, 123–125. Chen H., Roco M.C., Son J., Jiang S., Larson C.A, Gao Q. (2013). Global nanotechnology development from 1991 to 2012: Patents, scientific publications, and effect of NSF funding. Journal of Nanoparticle Research. 15, 1951. Chesbrough H.W., Vanhaverbeke W., West J. (Eds). (2006). Open Innovation: Researching a New Paradigm. Oxford University Press: Oxford. Coccia M., Finardi U. (2012). Emerging nanotechnological research for future pathways of biomedicine. International Journal of Biomedical Nanoscience and Nanotechnology. 2(3/4), 299–317. Coccia M., Finardi U. (2013). New technological trajectories of non-thermal plasma technology in medicine. International Journal of Biomedical Engineering and Technology. 11(4), 337–356. Coccia M., Finardi U., Margon D. (2012). Current trends in nanotechnology research across worldwide geo-economic players. Journal of Technology Transfer. 37(5), 777–787. Didegah F., Thelwall M. (2013). Determinants of research citation impact in nanoscience and nanotechnology. Journal of the American Society for Information Science and Technology. 64(5), 1055–1064. Dodgson M. (2017). Innovation in firms. Oxford Review of Economic Policy. 33(1), 85–100. Dong X., Qiu X., Liu Q., Jia J. (2013) Bibliometric analysis of nanotechnology applied in oncology from 2002 to 2011. Tumor Biology. 34, 3273–3278. Dosi G. (1982). Technological paradigms and technological trajectories. A suggested interpretation of the determinants and directions of technical change. Research Policy. 11(3), 147–162. Drexler K.E. (1992). Nanosystems: Molecular Machinery, Manufacturing and Computation. John Wiley & Sons: Chichester, ISBN 0-471-57547-X. Finardi U. (2011). Time relations between scientific production and patenting of knowledge: The case of nanotechnologies. Scientometrics 89, 37–50. Finardi U. (2012). Nanosciences and nanotechnologies: Evolution trajectories and disruptive features. In: Disruptive Technologies, Innovation and Global

1-13 Redesign: Emerging Implications, N. Ekekwe and N. Islam (Eds). IGI Global: Hershey, PA, ISBN 9781466601345. Finardi U. (2013a). The technological paradigm of Nanosciences and Technologies: A study of sciencetechnology time and space relations. Economía Teoría y Práctica. 39, 11–29. Finardi U. (2013b). Clustering research, education, and entrepreneurship: Nanotech innovation at MINATEC in grenoble. Research-Technology Management. 56(1), 16–20. Finardi U., Sprio S. (2012). Human bone regeneration from wood: A novel hierarchically organised nanomaterial. International Journal of Healthcare Technology and Management. 13(4), 171–183. Genet C., Errabi K., Gauthier C. (2012). Which model of technology transfer for nanotechnology? A comparison with biotech and microelectronics. Technovation. 32(3–4), 205–215. Grieneisen M.L., Zhang M. (2011) Nanoscience and nanotechnology: Evolving definitions and growing footprint on the scientific landscape. Small. 7(20), 2836–2839. Grinin L.E., Grinin A.L., Korotayev A. (2017). Forthcoming Kondratieff wave, cybernetic revolution, and global ageing. Technological Forecasting and Social Change. 115, 52–68. Guan J., Liu N. (2014). Measuring scientific research in emerging nano-energy field. Journal of Nanoparticle Research. 16, 2356. Hendren C.O., Mesnard X., Dröge J., Wiesner M.R. (2011). Estimating production data for five engineered nanomaterials as a basis for exposure assessment. Environmental Science and Technology. 45(7), 2562–2569. Huang C., Notten A., Rasters N. (2011). Nanoscience and technology publications and patents: A review of social science studies and search strategies. The Journal of Technology Transfer. 36(2), 145–172. Huang Y., Ma J., Porter A.L., Kwon S., Zhu D. (2015). Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer. Beilstein Journal of Nanotechnology. 6, 1666– 1676. Jafari M., Zarghami H.R. (2016). Measuring nanotechnology development through the study of the dividing pattern between developed and developing countries during 2000–2014. Journal of Nanoparticle Research. 18, 180. Jürgens B., Herrero-Solana V. (2017). Monitoring nanotechnology using patent classifications: An overview and comparison of nanotechnology classification schemes. Journal of Nanoparticle Research. 19, 151. Kline S.J., Rosenberg N. (1986). An overview of innovation. In: The Positive Sum Strategy: Harnessing Technology for Economic Growth, R. Landau and N. Rosenberg (Eds). National Academy Press: Washington, D.C., pp. 275–305.

1-14 Kondratieff N.D. (1935).The Long Waves in Economic Life, The Review of Economics and Statistics, Vol. 17, No. 6, pp. 105–115 Kreuchauff F., Teichert N. (2013). Nanotechnology as general purpose technology. Proceedings of ISSI 2013: 14th International Society of Scientometrics and Informetrics Conference, Vol. 2, 1291–1306, Vienna; Austria, 15 July 2013 through 20 July 2013. Mangematin V., Walsh S. (2012). The future of nanotechnologies. Technovation. 32(3–4), 157–160. Motoyama Y., Eisler M.N. (2011). Bibliometry and nanotechnology: A meta-analysis. Technological Forecasting and Social Change. 78(7), 1174–1182. Muñoz-Écija T., Vargas-Quesada B., Chinchilla-Rodríguez Z. (2017). Identification and visualization of the intellectual structure and the main research lines in nanoscience and nanotechnology at the worldwide level. Journal of Nanoparticle Research. 19, 62. OECD. (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, Third Edition. A joint publication of OECD and Eurostat. Available online at: www.oecd.org/sti/inno/oslomanualguidelinesfor collectingandinterpretinginnovationdata3rdedition.htm and: http://ec.europa.eu/eurostat/documents/3859598/ 5889925/OSLO-EN.PDF (links visited May 2016). Ozcan S., Islam N. (2014). Collaborative networks and technology clusters: The case of nanowire. Technological Forecasting and Social Change. 82(1), 115–131. Ozcan S., Islam N. (2017). Patent information retrieval: Approaching a method and analysing nanotechnology patent collaborations. Scientometrics. 111(2), 941–970. Pellegrino E.M., Cerruti L., Ghibaudi E.M. (2016). Realizing the promise-the development of research on carbon nanotubes. Chemistry-A European Journal. 22(13), 4330–4335. Piccinno F., Gottschalk F., Seeger S., Nowack B. (2012). Industrial production quantities and uses of ten engineered nanomaterials in Europe and the world. Journal of Nanoparticle Research. 14, 1109. Roco M.C., Mirkin C.A., Hersam M.C. (2011). Nanotechnology research directions for societal needs in 2020: Summary of international study. Journal of Nanoparticle Research. 133, 897–919. Russo A., Bianchi M., Sartori M., Boi M., Giavaresi G., Salter D.M., Jelic M., Maltarello M.C., Ortolani A., Sprio S., Fini M., Tampieri A., Marcacci1 M. (2018). Bone regeneration in a rabbit critical femoral defect by means of magnetic hydroxyapatite macroporous scaffolds. Journal of Biomedical Materials Research B: Applied Biomaterials. 106B(2), 546–554. Scaringella L., Chanaron J.-J. (2016). Grenoble–GIANT territorial innovation models: Are investments in research infrastructures worthwhile? Technological Forecasting and Social Change. 112, 92–101.

Public Policy, Education, and Global Trends Schumpeter J.A. (1911). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle. Transaction Publishers: New Brunswick, NJ and London. Schumpeter J.A. (1928). The instability of capitalism. Economic Journal. 38(81), 361–386. Schumpeter J.A. (1939). Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. McGraw-Hill: New York. Schumpeter J.A. (1942). Capitalism, Socialism and Democracy. Harper & Row: New York. Sotudeh H., Khoshian N. (2014). Gender, web presence and scientific productivity in nanoscience and nanotechnology. Scientometrics. 99(3), 717–736. Suominen A., Li Y., Youtie J., Shapira P. (2016). A bibliometric analysis of the development of next generation active nanotechnologies. Journal of Nanoparticle Research. 18, 270. Tampieri A., Iafisco M., Sprio S., Ruffini A., Panseri S., Montesi M., Adamiano A., Sandri M. (2016). Hydroxyapatite: From nanocrystals to hybrid nanocomposites for regenerative medicine. In: Handbook of Bioceramics and Biocomposites, I.V. Antoniac (Ed). Springer International Publishing: Switzerland, ISBN 978-3-319-12459-9. Terekhov A.I. (2017). Bibliometric spectroscopy of Russia’s nanotechnology: 2000–2014. Scientometrics. 110(3), 1217–1242. Vaithylingam R., Ansari M.N.M., Shanks R.A. (2017). Recent advances in polyurethane-based nanocomposites: A review. Polymer-Plastics Technology and Engineering. 56(14), 1528–1541. Wonglimpiyarat J. (2005). The nano-revolution of Schumpeter’s Kondratieff cycle. Technovation. 25(11), 1349– 1354. Xin Y., Huang Q., Tang J.-Q., Hou X.-Y., Zhang P., Zhen Zhang L., Jiang G. (2016). Nanoscale drug delivery for targeted chemotherapy. Cancer Letters. 379, 24–31. Youtie J., Shapira P. (2017). Exploring public values implications of the I-Corps program. The Journal of Technology Transfer. 42(6), 1362–1376. Zalewska-Kurek K., Egedova K., Geurts P.A.Th.M., Roosendaal H.E. (2018). Knowledge transfer activities of scientists in nanotechnology. The Journal of Technology Transfer. 43(1), 139–158. Zheng J., Zhao Z., Zhang X., Chen D., Huang M. (2014). International collaboration development in nanotechnology: A perspective of patent network analysis. Scientometrics. 98(1), 683–702. Zhu H., Jiang S., Chen H., Roco M.C. (2017). International perspective on nanotechnology papers, patents, and NSF awards (2000–2016). Journal of Nanoparticle Research. 19, 370. Zibareva I.V., Vedyagina A.A., Bukhtiyarova V.I. (2014). Nanocatalysis: A bibliometric analysis. Kinetics and Catalysis. 55(1), 1–11.

2 Policy and Innovation: An Invisible Evolving Nanoworld 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 What Is Nanotechnology? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Why Is There Such Great Attention to the Field of Nanotechnology and Nanomaterials? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 What Is Nanomatter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Which Techniques Are Used to Assess Nanomatter Characteristics/Properties? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Biancamaria Baroli, Maria Francesca Matzeu, and Carla Serri Universita ` di Cagliari

2.1

2-2 2-4 2-5

What Techniques Are Used to Characterize Nanomaterial Fundamental Properties? • How Can Nanomaterial–Biological System Interactions Be Tested?

2.6 Are Production, Use, and Disposal Regulated? . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-12 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-12

Introduction

This contribution is structured in three sections. In the first one, we will explain what nanomaterial-based products are and how these impact our life and the global ecosystem. In the second part, we will provide a selection guide for physicochemical–biological analysis by commenting critical parameters affecting measurements. Finally, in the third section, we will summarize regulations and agencies related to the European Union (EU) legislation on nanomaterials and products. Throughout the contribution, a friendly communicative class-style is maintained. We selected online-available references, generally reviews, in order to give readers the opportunity to deepen their specific interests. References placed at the end, or at the very beginning, of a paragraph are intended for the entire paragraph. Text without references has to be intended as the authors’ experience-based comments or popular knowledge. If not otherwise stated, the noun “product” is used as a synonym of nanomaterial or nanomaterial-containing product. Website pages proposed within the text or tables were accessed, if not otherwise stated, in November 2018.

2.2

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What Is Nanotechnology?

Nowadays, the word “nanotechnology” has become a noun of general use, and it is not difficult to find different definitions in web search engines. We got 10,600,000 links within 0.45 s. However, we decided to quote only some

of them (www.understandingnano.com/nanotechnologydefinition.html (April 2018), www.merriam-webster.com/ dictionary/nanotechnology, www.nano.gov/nanotech-101/ what/definition, Martin 2006, Taniguchi 1974) as examples. Nanoscience and nanotechnology are the study and application of extremely small things and can be used across all the other science fields, such as chemistry, biology, physics, materials science, and engineering. The science of manipulating materials on an atomic or molecular scale especially to build microscopic devices (such as robots). Nanotechnology is the study and use of structures between 1 nanometer (nm) and 100 nanometers in size. Structures, devices, and systems having novel properties and functions due to the arrangement of their atoms on the 1 to 100 nanometer scale. Many fields of endeavor contribute to nanotechnology, including molecular physics, materials science, chemistry, biology, computer science, electrical engineering, and mechanical engineering. Nanotechnology is the study of phenomena and fine-tuning of materials at atomic, molecular and macromolecular scales, where properties differ significantly from those at a larger scale. Products based on nanotechnology are already in use and analysts expect markets to grow by hundreds of billions of euros during this decade.

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Public Policy, Education, and Global Trends Nanotechnology is the understanding and control of matter at dimensions between approximately 1 and 100 nanometers, where unique phenomena enable novel applications. Encompassing nanoscale science, engineering, and technology, nanotechnology involves imaging, measuring, modeling, and manipulating matter at this length scale. [Nanotechnology is] an upcoming economic, business, and social phenomenon. Nano-advocates argue it will revolutionize the way we live, work and communicate. . . . the processing, separation, consolidation, and deformation of materials by one atom or one molecule. The science and technology of diagnosing, treating and preventing disease and traumatic injury, of relieving pain, and of preserving and improving human health, using molecular tools and molecular knowledge of the human body. Nanomedicine refers to highly specific medical intervention at the molecular scale for curing diseases or repairing damaged tissues, such as bone, muscle, or nerve.

Definitions change depending on different focuses. However, we can recognize some keywords: nanometric scale, matter, different/new properties, products in the market, and money.

2.3

Why Is There Such Great Attention to the Field of Nanotechnology and Nanomaterials?

The main push for “nano” research has been the discovery that, below the 100-nm limit, the quantum and electronic confinement, and surface effects of the material prevail, thus causing the latter to possess new properties not seen in its bulk state (Roduner 2006, Zhang et al. 2003). In particular, chemical, thermal, mechanical, optical, electrical, and magnetic properties appeared to be “sizedependent” at the nanoscale (Filipponi and Shuterland 2010, Han et al. 2001, Ramrakhiani 2012). Some chemical (e.g., improved catalytic activity) and optical (e.g., localized surface plasmon resonance) properties are also “shape-dependent” (Filipponi and Shuterland 2010, Scher et al. 2016, Sepúlveda et al. 2009) and the latter “surrounding media–dependent” as well (Filipponi and Shuterland 2010, Sepúlveda et al. 2009). Recently, interactions at the interface between nanomatter and biological systems have been shown to be further influenced by the curvature of nano-objects, which will govern the competition between “chemical (acid-base and redox equilibrium, ligands-receptor binding) and physical interactions (Van der Waals, electrostatic interactions, hydrogen bonds)

acting on different length and energy scales” (Gonzalez Solveyra and Szleifer 2016, Tagliazucchi and Szleifer 2012). To explain what this really means, we would like to list some outcomes of these new properties. This list has to be intended representative and not limited to the followings (Bosco Balaguru and Jeyaprakash available online, Grange 2013, Khan et al. 2017, Ramrakhiani 2012). Melting point decreases with decrease in size; crystal structure has different lattice parameters when comparing bulk and nanosized materials; generally ionization energy increases with decrease in size; the “surface area/volume” ratio increases with decrease in size, which, in turn, increases rate, selectivity, and efficiency of catalytic reactions. Elastic modulus and density reduce by 30% or less with decrease in size; hardness or strength increases four to five times with decrease in size; other mechanical parameters such as “stress and strain” and “adhesion and friction” may also be affected by size reduction. The specific heat and thermal expansion coefficient may increase up to 50% or more with decrease in size. Sintering temperature decreases with decrease in size. The color and transparency of materials change with change in the size of nanoparticles (blue-shifted absorption) contained in them; confinement of photons and phonons in nanoparticles also affects their Raman spectra; electrical conductivity is reduced with decrease in the size of nanocrystals (and resistivity increases); superconducting materials become non-superconducting by decreasing their size. Moreover, decreasing the size of an object may also cause it to have an enhanced diffusivity, an increased oscillator strength, an increased luminescence, and superior soft magnetic properties in comparison to conventional bulk materials. These new properties may be translated into new products to enter the market. Once again, cited applications have to be intended representative of, but not limited to, what we are presenting in the text and Table 2.1. By now, applications span in different markets such as cosmetics, every branch of medicine (i.e., therapeutic agents, medical devices, antimicrobial products, pharmaceuticals, diagnostics, and theranostics), automobile sector, electronics, nanophotonics, “food production, processing, safety and packaging”, fuel and fuel cells, solar cells, batteries, supercapacitors, space, chemical sensor, sporting goods, fabrics, “painting, filming and coating”, ferrofluids, color imaging, bioprocessing, high storage density magnetic memory, high power magnets, processing and constructions, house-hold products, ecology, additive in pesticide, fertilizer, and plant protection formulations (Bhatia 2016, Bosco Balaguru and Jeyaprakash available online, Grange 2013, Guo et al. 2014, Jitendra et al. 2012, Khan et al. 2017, Ma et al. 2015, Ramrakhiani 2012, Shen et al. 2000, http://ec.europa. eu/health/scientific_committees/opinions_layman/en/nan otechnologies/l-3/5-nanoparticles-consumer-products.htm# 0p0, www.understandingnano.com/nanotech-applications. html (April 2018), www.understandingnano.com/nanotechnology-electronics.html (April 2018)).

Policy and Innovation

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Without entering in the specifics of each application area (some examples cited in proposed bibliographic references may be found in Table 2.1), it is clear that the nano-era will bring a revolution in our daily life and beyond. We previously mentioned that the upper limit of the nanometric scale is 100 nm, whereas the lower limit of a fabricated nano-object is 0.4 nm, to our knowledge (Attia et al. 2016). By considering that DNA helix and red blood cell diameters are, respectively, 2 and 7,000 nm, we can reasonably say that the dimensions of nanomaterials, in their broadest meaning, are comparable with those of viruses (10–150 nm) and molecules (e.g., in liquid state the H–O length of water molecules is ca. 0.1 nm) (Buzea et al. 2007, Adaya et al. 2017). Therefore, when they enter in the cells of living organisms, spanning from plankton, algae, and microorganism to humans, along the food chain (Ma et al. 2015), they may interact with cellular structures and regulation pathways. This, in turn, may cause profound changes that can exacerbate pre-existing pathologies in susceptible organisms, induce mortal pathologies, or maybe TABLE 2.1

even genetically heritable to further generations (Arora et al. 2012, Batley et al. 2013, Brown et al. 2004, Bundschuh et al. 2018, Buzea et al. 2007, Elsaesser and Howard 2012, Frampton et al. 2006, Hossain et al. 2015, Lei et al. 2018, Ma et al. 2015, Nemmar et al. 2002, Oberdörster et al. 2005, Shatkin and Ong 2016, Shi et al. 2018, Tripathi et al. 2017, Viswanath and Kim 2017). One could now question whether it is reasonable to state that nanomaterials may enter in contact with living species. Many researchers pondered on that. As a result, contamination cycles and toxicity mechanisms are being under intense study (Batley et al. 2013, Bundschuh et al. 2018, Hossain et al. 2015, Lei et al. 2018, Ma et al. 2015, Oberdörster et al. 2005, The Royal Society & The Royal Academy of Engineering 2004, Tripathi et al. 2017, Viswanath and Kim 2017). In 2017, Viswanath and Kim stated that nanoproducts could impact the environment through “(i) direct effect on invertebrates, micro-organisms, fish and other species; (ii) interaction with other pollutants, that may transform the bioavailability of toxic compounds

Examples of Nanomaterial Application Areas and Products

Areas Medicine

Example Drug delivery, drug targeting, vaccines, detoxificant nanosponges, devices for therapy, antimicrobial formulation/techniques, nanorobot, cell repair.

Cosmetics

Sunscreen; antioxidant products

Electronics

Flexible electronic circuit; nanomagnetic switches; silver nanoparticle ink; laser that uses a nanopatterned silicon surface; carbon nanotube–based transistors; integrated circuits built with/using nanotube transistors; flexible flat panels; semiconductor nanowires; graphene-based transistors and p-n junctions; Nanoparticle Organic Memory Field-Effect Transistor (NOMFET); “nanoemmissive” display panel; displays using quantum dots; 22-nm-wide transistor gates; Magnetoresistive Random Access Memory (MRAM); molecularsized transistors; self-aligning nanostructures to manufacture nanoscale-integrated circuits; transistors without p-n junctions; dense, low power memory devices; magnetic quantum dots for spintronic semiconductor devices; flexible conductive layers; flexible electronic circuits; silicon nanophotonic components; phosphors for high-definition TV and flat-panel displays.

Food

Nanocomposite coatings for transparent plastic films used in food packaging that provides a barrier to oxygen or moisture; rapid testing for contaminants in food; bottles, cartons, and films containing clay nanocomposite that act as a barrier to the passage of gasses or odors; silver to kill microorganism; sensors in packaging for spoiled food, UV ray filter in packaging.

Agrifood

Nanomaterials as additives in pesticide, fertilizer, and plant protection formulations.

Fuel cells

Different types of catalytic nanoparticles to produce hydrogen as fuel, development of light hydrogen containers

Air pollution

Nanocomposite catalyst to be used in automobile catalytic converters; catalyst for the removal of volatile organic compounds (VOC) in industrial air emissions; membranes for the removal of carbon dioxide from smokestacks; catalysis and gas separation membrane.

Water pollution

Trap, mad, filters, sponges to adsorb oil, oil spill, radioactive particles and ions, ions, antibiotics, mercury; nanoparticles to clean up groundwater from carbon, tetrachloride, and hydrophobic solvents; electrified filters to kill bacteria; antimicrobial nanofibers and activated carbon–based disposable filter to clean contaminated water; (photo)catalysis for (chlorinated) organic molecule.

Chemical sensors

Sensors for the environment and body (implantable) to detect cell, viruses, bacteria, organic molecules, nervine gas, and various pollutants.

Sporting goods

Nanocomposite barrier film to prevent air loss from balls etc.; produce tennis racquet frames with increased strength, stability, and power; coating for water vehicles providing increased abrasion resistance; badminton racquets or golf club shafts with increased power and stability; bicycle parts with increased stiffness without weight increase; ski wax with increased gliding performance and maximum speed; fishing rods with increased strength without weight increase.

Fabrics

Water and stain-resistant fabric; antimicrobial- and odor-resistant clothing; superior insulation for shoe inserts for cold weather; rain-cleaning outdoor fabrics.

Automobile sector

Tire-improved adherence; improved stiffness car body; transparent car body parts for an improved driver’s field of view; glass antireflection layers.

Painting/Filming/Coating

Scratchproof eyeglasses; crack-resistant paints; anti-graffiti coatings for walls; transparent sunscreens; stainrepellent fabrics; transparent sunscreens to be applied not only on human skin.

Processing and constructions

Self-cleaning glass/windows; UV resistant wood coating; tougher and harder cutting tools; ductile, machinable ceramics; lubricants; soft and precise abrasion/polishing products; nanofluids with advanced thermal conductivity.

House-hold products

Self-cleaning windows; antimicrobial products.

Ecology

Elimination of pollutants; better air quality, cleaner water, water disinfection, gas sensing, CO2 capturing, gas adsorbent for environmental remediation.

2-4 and nutrients; and (iii) changes to non-living environmental structures”. In humans, exposure to nanomatter could be either intentional (e.g., administration of a theranostic) or unintentional (Batley et al. 2013, Buzea et al. 2007, Gulson et al. 2015, Ma et al. 2015, Nemmar et al. 2002, Oberdörster et al. 2005, Viswanath and Kim 2017). In the latter case, we can hypothesize two main scenarios. (i) Individuals get in contact with nano-objects released, (non-)intentionally, in the environment (e.g., not-strictly controlled production, normal and industrial waste, aged products, washed products, products for the environment, contaminated food/water). (ii) Individuals adsorb nano-objects during the use of a nanomaterial (containing) product sold for external use (e.g., nanoparticles in smart fabrics penetrate skin; nanoparticles contained in a cosmetic are ingested or inhaled). In both cases, classical exposure routes are interested – lung, skin, and oralgastro-enteric tract (Arora et al. 2012, Buzea et al. 2007, Ciappellano et al. 2016, Fröhlich and Roblegg 2012, Gulson et al. 2015, Larese Filon et al. 2015, Oberdörster et al. 2005, Viswanath and Kim 2017) – but olfactory bulbs (Oberdörster et al. 2005) and eyes (Mun et al. 2014, Prow 2010) should not be forgotten. From these very first entries, nanomaterials have shown their abilities to cross several barriers, including placenta (Campagnolo et al. 2017, Juch et al. 2013, Müller et al. 2018, Muoth et al. 2016, Stapleton and Nurkiewicz 2014), and to use both blood and lymphatic circulations to move within and through tissues and organs according to their biokinetics (Arora et al. 2012, Buzea et al. 2007, Nemmar et al. 2002, Oberdörster et al. 2005, The Royal Society & The Royal Academy of Engineering 2004, Viswanath and Kim 2017). Recently, microbiota (i.e., bacteria, fungi, and viruses living in the gut mucus) and microbiome (all the genetic materials within an entire collection of microorganisms in a specific niche; www.nature.com/subjects/microbiome) are receiving a great attention, since both interact with the cells of host organisms to keep them healthy. In particular, microbiota/microbiome is/are involved in mucus layer shaping, food digestion, vitamin and amino acid synthesis, energy metabolism and storage, immune systems modulation, growth, neurodevelopment, behavior regulation, and disease onset upon microbiota composition alteration (by drugs, diet, and environmental pollutants). This latter observation is driving intense research to assess potential toxicity of nanomaterials on microbiota as regulator for organism pathologies onset/development (Bouwmeester et al. 2018, Ciappellano et al. 2016, Dudefoi et al. 2017, Jin et al. 2017, Karavolos and Holban 2016, Pietroiusti et al. 2016). To rise some further considerations on exposure, we would like to highlight that during an intentional contact, exposure time and dose might be strictly regulated, whereas in a non-intentional contact, nothing would be under control. Concurrent or repetitive exposure to multiple nano-objects (that could differ from their original production state due to ageing or interactions with other pollutants or biomass)

Public Policy, Education, and Global Trends from air, water, soil, or food chain can also occur. Therefore, it is worth suggesting that the dose of nano-objects per day could increase substantially. If nano-objects under considerations are toxic, persistent, and/or bioaccumulative, toxicological risks become more than a hypothesis (Bundschuh et al. 2018, Buzea et al. 2007, Oberdörster et al. 2005, The Royal Society & The Royal Academy of Engineering 2004, Viswanath and Kim 2017). Therefore, alongside with advancements in basic nanosciences and applications, nano(eco)toxicology and analytical methods/equipment are receiving great attention as well. National and International Actors/Agencies on Health and Environment Protection are hence pushed to define internationally common rules for a safe production, use, and disposal of nano-objects (Arora et al. 2012, Bundschuh et al. 2018, Gulson et al. 2015, Hossain et al. 2015, Oberdörster et al. 2005, Shatkin and Ong 2016, The Royal Society & The Royal Academy of Engineering 2004, Viswanath and Kim 2017). Nevertheless, resistances arising from such a huge global market (estimated in trillions of US dollar and with thousands of products already in the market) may somehow slow down this poetic view of scientific advancements.

2.4

What Is Nanomatter?

Nanomatter is a very general term used to indicate “objects” sized in the nanometric scale without giving details on their characteristics and/or properties. In fact, we could start saying that “sized in the nanometric scale” can be better defined by indicating how many dimensions of the “object” (please visualize the object within a XYZ Cartesian system) do not lay in the nanometric scale (100 nm 0

Classification 0D nanomaterial

Examples Nanoparticles, uniform particles arrays (quantum dots), heterogeneous particles arrays, core–shell quantum dots, core–shell nanoparticles, onions, hollow spheres and nanolenses, hollow cubes, nanospheres.

1

1D nanomaterial

Nanotubes, nanorods, nanowires, nanobelts, nanoribbons, nanofibers and hierarchical nanostructures.

2

2D nanomaterial

Objects with plate-like shape: graphene, nanofilm, nanolayers, nanocoatings, junctions (continuous islands), branched structures, nanoplates, nanosheets, nanowalls, nanodisks, nanoprisms, gold DNA arrays, gold nanobowls arrays, arrays of hollow spheres, dendrite consisting of nanorod bundles, 2D hexagonal star-like beta-MnO2 and dendrite-like hierarchical beta-MnO2 nanostructures, nanoplatelets.

3

3D nanomaterial

Bulk powders, nanocomposites, dispersions of nanoparticles, bundles of nanowires, bundles of nanotubes, multi-nanolayers, nanoballs (dendritic structures), nanocoils, nanocones, nanopillers, nanoflowers, 3D grids of multiplex zigzag nanowires, 3D urchin-like nanostructured aluminum nitride (AlN), highly compressed and regular coiled carbon nanotubes (CCNTs), 3D dendritic nanostructures, 3D hierarchical nanostructures.

amorphous structures (Bhatia 2016, Viswanath and Kim 2017). The composition of inorganic nanomaterials may be based on carbon atoms (e.g., carbon nanotubes, graphene, and fullerenes), metal oxides (e.g., SiO2 , Fe2 O3 , Fe3 O4 , ZnO, TiO2 , MnO), semiconductor materials (e.g., quantum dots, Cornell dots), zero-valent metals (e.g., gold, silver), nanopolymers (e.g., phyllosilicates and nanoclays; Schoonheydt and Bergaya 2011). In contrast, dendrimers, micelles, macromolecules, bio-colloids, cell debris, some viruses, proteins, and nucleic acids belong to organic nanomatters (Bhatia 2016, Viswanath and Kim 2017).

2.5

Which Techniques Are Used to Assess Nanomatter Characteristics/Properties?

Generally speaking, properties of nanomaterials may be differentiated as fundamental (physico-chemical properties, e.g., size, shape, and superficial area), characterizing (typical of a family of products, e.g., luminescence and magnetism), and biological/toxicological (e.g., nano–bio-kinetics and nano–bio-dynamics, interaction with membranes, barriers, cells, cell structures, tissues and organs, toxicity, bioaccumulation, persistency, lethal dose, cancerogenicity). In Table 2.3, we summarized some typical properties reported in articles related to human/animal/cell exposure, which readers may find in the “References” section. In addition, Tables 2.4 and 2.5 have to be intended as selection guides for analytical and biological measurements, respectively. TABLE 2.3

It is worth noting that, for any nanomaterial, it will be the specific fundamental properties – as a whole – that will govern all the other ones. In addition, biological and toxicological outcome may be only supposed if fundamental properties are not re-measured in the specific biological/toxicological conditions under examination. For instance, size, aggregation state, surrounding coating (both presence and composition), and chemical composition may vary within days until reaching an equilibrium when a nanomaterial is exposed to biological media (Barrán-Berdón et al. 2013, Hadjidemetriou and Kostarelos 2017, Tenzer et al. 2013, Wolfram et al. 2014, Wang et al. 2016). These modifications may be important not only to explain the mechanism(s) of cell entrance but also general nanobiokinetics and nano-biodynamics. The ability of a nanomaterial to release ions in biological media for simple contact (e.g., dissolution and corrosion) or in a biological defensive environment (e.g., inside the phagolysosome), or maybe triggered by concurrent physical parameters (e.g., sun exposure, warm/cold climate exposure) should also be assessed. This knowledge will be important when one has to relate nanomaterial exposure with mechanism(s) of potential toxic effects. Several nanomaterial parameters, such as chemical composition, size, shape, density, and surface area and how they change in biological media are important when indicating administered dose. This information is extremely important to critically compare the published articles. One could question why all these parameters are needed to express the units of a dose, which is generally given in “mass/volume” units. Even in this case, in the real

Nanomaterial Properties

Properties Fundamental

Type Physical–chemical

Example Chemical composition; lattice structure; size and size distribution; shape; surface area; surface charges; porosity; density; number concentration; state of aggregation/aggregability in different media; solubility; dispersability; presence of a coating/coatability in different media; movement freedom; degradability.

Characterizing

Physical–chemical

Luminescence; fluorescence; plasmon resonance; blue shifted absorption; superparamagnetism, magnetism; catalytic ability; various mechanical properties.

Biological

Antimicrobial activity; antioxidant activity.

Biological/Toxicological

Physical–chemical–biological

Inflammation; reactive oxygen/nitrogen species generation; oxidative stress, apoptosis; lipid peroxidation; DNA damage; cell-cycle alteration; production of neoantigens; epithelium permeation; migration in blood and lymphatic circulation; blood–brain barrier permeation; toxicity, bioaccumulation, persistency, lethal dose, carcinogenicity, primary and secondary genotoxicity, antigenicity.

High-resolution topography, 3D visualization (shape), size, atomic morphology, mechanical properties, “atom identification, interaction, manipulation”, single-cell stimulation, adhesion strength, conductivity, surface potential Elemental analysis, composition, trace element

Solid materials Liquid dispersion Inorganic, organic, biological specimens

Liquid, gas samples Extracted or digested solid samples Inorganic, organic, biological samples

Dry inorganic, organic, biological samples

Atomic Force Microscopy (AFM) Scanning Force Microscopy (SFM)

Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES)

Energy-dispersive X-ray spectroscopy (EDX, EDS, EDXS, or EXDS)

Elemental analyses (spot or area) of different matrices Chemical characterization Relative element abundance (semi-quantitative) Coating layer thickness measurement

Morphology of surface (2D), size, size distribution, shape Distribution of nanomaterials within biological specimens

Dry inorganic, organic, biological specimens

Morphology of surface (3D), size, size distribution, polydispersity of size distribution, shape

Surface charges, aggregability potential

Clean, filtered, degassed, and transparent aqueous or organic dispersions Dry inorganic, organic, biological specimens

Transmission Electron Microscopy (TEM)

Scanning Electron Microscopy (SEM)

Purpose Hydrodynamic size, size distribution, polydispersity (PDI) of size distribution, size stability kinetics

Sample Clean, filtered, degassed, and transparent aqueous or organic dispersions

Nanomaterial Fundamental Properties: Assay Selection

Analysis Dynamic Light Scattering (DLS) Photon Correlation Spectroscopy (PCS) Quasi-elastic Light Scattering (QELS) Zeta Potential (PZ)

TABLE 2.4

‘exit modelKitMode’. Solvent and ligand molecules as well as counterions may be part of an experimental CIF of a small or protein molecule (Scalfani et al. 2016) as mentioned above in Section 14.4.2 but are typically not present in inorganic crystal structures. (Crystal water is, on the other hand, a wellknown integral part of certain mineral structures.) Note that our converter tool allows often for the suppression/removal of typical solvent molecules and counterions from the interactive display at the website and from the produced 3D print file simply by the pressing of the ‘Largest Molecule’ button.

Public Policy, Education, and Global Trends That procedure does, however, not work for mechanically interlocked molecules such as the one discussed below, because these bonds represent a recent addition to supramolecular chemistry, see Sir Fraser Stoddart’s Nobel Prize lecture (Stoddart 2017). Since the structure of right-α-quartz is rather simple with only three atoms in the chemical formula unit, we tested the ‘PSU 3D Converter’ also with the much more sophisticated structure of a doubly interlocked [2]catenane molecule, Figure 14.15. The chemical (sum) formula of that molecule (including solvents and counterions) was experimentally derived to be C130 H144 F36 N16 O36 P4 Pd2 S4 (Peinador et al. 2009). Such molecules have been referred to as Solomon Knots/Links in the popular culture after King Solomon of the Old Testament. Only a few mechanically interlocked catenane molecules have so far been synthesized, see Sir Fraser Stoddart’s Nobel lecture (Stoddart 2017). The experimentally determined atomic structure of that [2]catenane molecule including solvent molecules and counterions is displayed in JSmol on the left-hand side of Figure 14.15. The experimentally derived CIF of this molecule was obtained from the supporting material of the paper by Peinador et al. (2009) and read into the ‘PSU 3D Converter’. Our converter tool was then used to prepare an X3D file for this molecule, which was sent as an e-mail attachment to 3D Systems Corporation, where it was printed out, see the center of Figure 14.15. The removal of (unconnected) solvent molecules and counterions could, for this doubly interlocked molecule, not be obtained by clicking on the ‘Largest Molecule’ button of the ‘PSU 3D Converter’ so that we removed them with JSmol editing functions that are accessible over

FIGURE 14.15 (a) JSmol display of the experimentally derived atomic structure of a doubly interlocked [2]catenane molecule (including counterions and solvent molecules) as present in a CIF that is freely available (Peinador et al. 2009). (b) 3D printed ball-andstick model of the doubly interlocked molecule as obtained with a 3D Systems ProJet MJP 2500 printer in the highly flexible ‘engineering Armor’ material. Note that the model is highly durable and fracture resistant even with the numerous small features. The model measures approximately 25 cm along its largest dimension. (c) JSmol display of the two otherwise doubly interlocked components of this molecule in separated form.

3D Printing at the College/University Level the ‘Set Picking’ menu and its sub-menus (as described above). When one presses the ‘Largest Molecule’ button in our converter tool, Figure 14.13, with the CIF of the doubly interlocked [2]catenane molecule read-in, one obtains only the ‘separated molecule component’ as shown in the lower right part of Figure 14.15. The ‘molecule by number’ button of Bob Hanson’s ‘Jmol Crystal Symmetry Explorer’, Figure 14.16, facilitates, on the other hand, the separate visualization of both components of the doubly interlocked molecule, from which separate 3D print files can then be exported.

14.4.6

Other Resources around 3D Print Files of Atomic-Level Small Molecule and Crystal Structure Models

The ‘PSU 3D Converter’ as discussed in the previous section is based on Bob Hanson’s freely available ‘Jmol Crystal Symmetry Explorer’ (Hanson 2016a), Figure 14.16, which is part of his well-known Jmol/JSmol suite of interactive atomic structure visualization/editing tools (Hanson 2010). Note that JSmol is the non-Java HTML 5 version of Jmol. The ‘Jmol Crystal Symmetry Explorer’, Figure 14.16, possesses the same interactive Jmol/JSmol visualization

14-15 and editing functionality as the ‘PSU 3D Converter’ but many more ready-made ‘shortcut buttons’ at the website level. It was also used for the programmatic conversion of more than 30,000 CIFs from the COD (entries between id 1000000–1999999 and 2000000–2999999) into 3D print files of small molecule models (Scalfani et al. 2016). The scripting capabilities of the Jmol/JSmol suite were utilized for that purpose. These 3D print files can be downloaded for free from the Figshare platform (Scalfani 2016). It is also possible to access these ready-made 3D print files from a dedicated website (Hanson 2016b) that features a search surface similar to that of the COD. Bob Hanson’s ‘JSmol 3D print’ website (Hanson 2016b) allows also for both searches of the whole COD (from its inbuilt search surface) and the creation of 3D print files from any CIF in the COD or any userprovided CIF (Scalfani et al. 2016). Note that files in the X3D format can be exported from the website of the ‘JSmol Crystal Symmetry Explorer’, while most other routes end currently only with STL or VRML files. While the first of these two file formats is the industry wide standard for monochrome 3D printing, VRML and X3D allow for color prints in 3D as already mentioned above. STL files can become very large for highly sophisticated models and high resolution as mentioned above, but X3D

FIGURE 14.16 Screenshot of the ‘Jmol Crystal Symmetry Explorer’ at its original location with the content of one ‘packed’ unit cell of right-α-quartz (projected down the c-axis) displayed in the JSmol visualization/editing space (with the unit cell outline displayed).

14-16 files for the same models are typically only about one-tenth of the size of files in the former format and can, therefore, still be sent as e-mail attachments (as we have done ourselves, as mentioned above). Note also that Bob Hanson has incorporated (STL, X3D, and VRML) 3D print file export capabilities also into his Jmol/JSmol releases from version 14.6.4 onward (Hanson 2016c). Any website that features a Jmol/JSmol workspace based on any of those recent releases allows, thus, for the interactive creation of 3D print files of models with atomic level details. There is, thus, a generic route from crystallographic information in multiple formats (Hanson 2016c) and CIFs to 3D print files of crystal and molecule structures that is independent of the operating system of the user’s computer. The Jmol/JSmol-featuring websites of the openaccess COD, wwPDB, and AMCSD as well as those of the (subscription based) Cambridge Structural Database (CSD, Groom et al. 2016, Bruno et al. 2017), where access to individual small molecule structure data files is unrestricted, will all allow for the remote creation of 3D print files from inbuilt Jmol/JSmol workspaces after necessary upgrades (Scalfani et al. 2016). ‘Classroom 3D printing’ is, therefore, at present probably as mature as it may become thanks to the efforts of numerous educators at the college/university level who volunteered their time over recent years to reach this state of affairs. The curators of the CSD provide also a route to 3D prints from CIFs using a stand-alone program (Wood et al. 2017). Their highly venerable (but freely useable) molecule and crystal-structure visualization program ‘Mercury’ (Macrae et al. 2008), that is available for Windows, Linux, and MacOS platforms, can be used for this purpose (after upgrades to versions released in 2016 and later). ‘Mercury’ allows also for the removal of solvent molecules and counterions that a CIF might contain from both the CIF and the 3D print files that are created from it. We employed these editing capabilities of ‘Mercury’ to prepare a ‘cleaned up’ CIF for the doubly interlocked [2]catenane molecule mentioned in the latter part of Section 14.4.5. That ‘cleaned up’ CIF of this molecule was read into the Cif2VRML program, and 3D print files were exported by means of the proverbial ‘one click’ (Kaminsky et al. 2014). We also tested the 3D print file creation process of ‘Mercury’ on the basis of the ‘cleaned up’ CIF of this molecule. As a result of these tests and our report in the latter part of Section 14.4.5, we can confidently state here that ‘mechanical bonds’ (Stoddart 2017), which exist as a novel type of bonds between different parts of mechanically interlocked molecules, do not present difficulties to our JSmol-based 3D converter tool, to the Cif2VRML software, and to the Mercury software. These kinds of bonds should also not present difficulties to any modern 3D printer that uses support materials that are to be removed from the final printed-out model. Note finally in passing that the bulk of the considerable amount of work of putting hundreds of thousands of CIFs (Bruno et al. 2017) into open access and of developing

Public Policy, Education, and Global Trends straightforward routes for the creation of 3D print files from them was done by largely unpaid volunteers (as they were mostly University/College faculty members and their students). That kind of work over more than 15 years did, however, ‘force’ the commercial CSD to add 3D print file creation capabilities to their ‘Mercury’ program from the summer of 2016 onwards. The CSD staff’s assertion that it is their recent work that ‘truly democratizes the use of 3D printing for experimentally accurate molecular, intermolecular and supra-molecular models’ (Wood et al. 2017) is, therefore, quite unfounded.

14.5

Summary and Conclusions

The literature on 3D printed models for STEM education at the college/university level was reviewed. The prediction by the Gartner consulting company of taking more than 10 years (from July of 2014 onward) for ‘Classroom 3D Printing’ to reach its ‘Plateau of Productivity’ in one of their hallmark ‘Hype Cycle’ graphs was critically assessed. Current methodologies and best practices of college level ‘Classroom 3D Printing’ were described in the main section of this chapter. Based on the quite mature state of the affairs as described in that section, it is concluded that Gartner’s prediction lacks optimism and seriously underestimates the creativity and resourcefulness of college/university educators as well as their commitment to their students. Microsoft Windows™ executable computer programs for the creation of 3D print files of small molecules and proteins, crystal morphologies, and (longitudinal effect) representation surfaces of tensor properties of crystals were reviewed. A straightforward route from crystallographic information framework files (CIFs at the Open-Access Crystallography project of Portland State University’s Nano-Crystallography group) to 3D print files for atomic-level crystal and molecule structure models was described in some details. Only the exported 3D print files are downloaded from the website of the ‘PSU 3D Converter’ as no local installation of any supporting program or applet is necessary. Openly accessible depositories of 3D print files and a few other creation routes for STEM education at the college level were also mentioned.

Acknowledgments Financial support from both Portland State University’s Faculty Enhancement program and the US National Committee for Crystallography is acknowledged. Robert van der Meulen of the Gartner consulting company is thanked for both Figure 14.1 and for communicating Gartner’s definition of ‘Classroom 3D Printing’ to the first author. 3D Systems Corporation is thanked for facilitating the 3D printing of the models in Figures 14.3, 14.5, 14.6, 14.7, 14.9, 14.11, 14.14, and 14.15, as well as for donating Cube 3 printers to WK and PM. We are grateful to Maria Kaminsky, who created the 3D printed models that are

3D Printing at the College/University Level shown in the centers of Figures 14.3 and 14.6 with a Prusa i3 mk2 printer after substantial print parameter optimizations. Prof. Bob Hanson of St. Olaf College, Northfield/Minnesota, is thanked for both his great work over many years in connection with the development of Jmol/JSmol and his generosity as demonstrated by him making the results of these developments openly available. The countless contributors to the COD and AMCSD are thanked for their uploading of crystallographic data sets for the greater good.

Appendix: Brief 3D Printing Technology Review There are numerous additive manufacturing technologies commonly used today. Over the past three decades, major advances have been made and numerous new techniques have been developed. Each of these technologies achieves the basic value proposition of 3D printing, but each has also unique capabilities in terms of material properties, part attributes, as well as costs and printing speeds. This chapter shows examples from a few of the more common technologies used for rapid prototyping. Both ‘hobby’ grade Fused Filament Fabrication (FFF) and ‘professional grade’ 3D printed models, as obtained by the Selective Laser Sintering (SLS® ), MultiJet Printing (MJP® ), and ColorJet Printing (CJP® ) technologies of 3D Systems Corporation are shown. Each of these technologies operates by dramatically different physical processes. Many consider Charles Hull the inventor of 3D printing. His company, i.e. 3D Systems Corporation, created the very first commercial 3D printer in the late 1980s using the Stereolithography (SLA® ) process that he had invented. SLA® exposes photo-polymers to radiation (which is typically ultraviolet light). The radiation triggers a chemical reaction within the material, causing curing of the polymer. SLA® systems print with supports, are advantageous due to the speed and possible size of prints – both large and small, and can rapidly manufacture parts of different geometries at the same time. They are designed to produce both prototypes and end-use parts of versatile sizes and applications. The SLA® technology is particularly good for parts requiring optical clarity. SLA® parts are strong enough to be machined and can be used as master patterns for injection molding, thermoforming, blow-molding, and in various metals casting techniques. The Selective Laser Sintering (SLS® ) method fuses powder materials layer by layer until the structure is built. To do this, a layer of material is spread evenly over a bed. Selected sections of this powdered layer are laser-fused by complete or partial melting. SLS® can be used for a wide range of powder materials, including different types of plastics, metals, ceramics, as well as glass, and can produce structures with high geometric complexity. It is also robust to complex overhangs due to an inherent support structure that is created by the powdered bed. SLS® uses traditional

14-17 material powders as the raw material and is well known to achieve sufficient physical properties for end-use parts similar to those traditionally manufactured by injection molding. Direct Metal Sintering (DMS® ) refers to 3D Systems Corporation’s metal-printing process. This process is similar to SLS® but uses metal powders rather than plastics. This technology can be overly expensive for most educational purposes and is used primarily in medical and aerospace applications, where low volumes of unique and complex models are needed. The Color Jet Printing process (CJP® ) uses inkjet technology to deposit a liquid binder across a bed of powder. The powder is released and spread with a roller to form each new layer. CJP® creates large-build prints in spectacular trueto-life color and is, therefore, ideal for educational uses due to its ability to make color models with sufficient material properties mixed with a combination of fast printing speed and low run cost. The Multijet™ printing process (MJP® ) utilizes a high precision 3D inkjet printing process. This ink-jet technology is combined with wax/resin and/or UV curable materials to produce highly detailed and accurate physical prototypes. High resolution is attainable using a support material that can be easily removed by post-processing. MultiJet™ printing is an extremely easy to use and versatile technology because the supports are automatically created and are easy to post process. Such printers can utilize materials with a wide range of mechanical properties. New developments have introduced materials with exceptional mechanical stability simulating the performance of acrylonitrile butadiene styrene and polypropylene, e.g. the abovementioned ‘engineering Armor’, thus turning MJP® into a very good printing process that serves the needs of educators. Extrusion-based printing, i.e. the FFF technique, consists of the deposition of melted thermoplastics in layers. A bed is placed underneath a heated nozzle which then extrudes molten plastic onto the bed. This technology is ideal for hobbyist and consumer printing for educational needs due to the available low-cost machines and materials as well as relatively simple and safe printing and postprocessing processes. 3D printed surfaces can, however, be somewhat rough when created with the FFF technology, and a model may suffer from the visibility of ‘layer lines’. The 3D prints that are obtained with this technology are nevertheless typically good enough for many educational uses. So many 3D printing technologies exist because of the numerous different valuable attributes needed by many different kinds of paying customers. It is easy to understand that any technology that has withstood marketplace pressures for some time provides, by definition, unique and value-added features and/or capabilities. Until those unique value propositions are mitigated by a single technology, numerous different technologies will remain for the user to choose from.

14-18

Notes Added in Proof 1. This book chapter was drafted in May of 2017 and slightly revised, e.g. references updated, about a year later. References to more recent papers and reports are therefore absent. We would, however, like to bring a recent review by J. Pernaa and S. Wiedmer, https://www.degruyter.com/view/j/ cti.ahead-of-print/cti-2019-0005/cti-2019-0005.xml (DOI: 10.1515/cti-2019-0005), to the attention of our readers. Their review features the title “A systematic review of 3D printing in chemistry education – analysis of earlier research and educational use through technological pedagogical content knowledge framework”. It is in open access, available on line, and to be published in the journal “Chemistry Teacher International” in 2020. 2. 3D Systems Corporation’s MJP printing technology produces by now over 1.1 billion droplets per cubic inch and is able to create extremely high fidelity parts with smooth surfaces. Recently developed print materials for this technology have enabled a wide range of improved material properties, including so-called Engineering, Elastomeric, and Specialty materials (where the latter enable high temperature applications). The so-called Engineering materials feature exceptional mechanical stability and toughness, simulating the performance of acrylonitrile butadiene styrene (ABS) and polypropylene. The Engineering materials Armor and Proflex may serve here as examples. The combination of ease of use, high spatial resolution, and tailorable material properties enables the MJP print technology to serve the needs of educators/researchers over a broad range of education/research applications. 3. Color versions of all of the figures in this book chapter are available at arXiv:2001.04267. 4. The Crystallography Open Database, http:// www.crystallography.net/cod/, features by now more than 452,500 CIF entries. 5. By now there is a new release of the Mercury program, which is described in Macrae et al. (2020).

References Bain, G. A., Yi, J., Beikmohamadi, M., et al. 2006. Using physical models of biomolecular structures to teach concepts of biochemical structure and structure depiction in the introductory chemistry laboratory. J. Chem. Educ., 83: 1322–1324. Berman, H. M., Kleywegt, G. J., Nakamura, H., et al. 2014. The protein data bank archive as an open data

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14-19 Hanson, R. M. 2016b. Jmol 3D print. http://chemapps. stolaf.edu/jmol/3dprint/, assessed May 14, 2018. Hanson, R. M. 2016c. https://sourceforge.net/projects/jmol/ ?source=navbar, assessed May 14, 2018. Hart, G. W. 2005. Creating a mathematical museum on your desk. Math. Intelligencer 27: 14–17. Herman, T., Morris, J., Colton, S., et al. 2006. Tactile teaching: Exploring protein structure/function using physical models. Biochem. Mol. Biol. Edu. 34: 247–254. Higman, C. S., Situ, H, Blacklin, P. et al. 2017. Hands-on data analysis: Using 3D printing to visualize reaction progress surfaces. J. Chem. Educ. 93: 1586–1590. Horowitz, S. S. and Schultz, P. H. 2014. Printing space: Using D printing of digital terrain models in geosciences education and research. J. Geosci. Educ. 62: 138–145. Huang, Y., Leu, M. C., Mazumder, J. et al. 2015. Additive manufacturing: Current state, future potential, gaps and needs, and recommendations. J. Manuf. Sci. Eng. 137: 014001 (10 pages). Jittivadhna, K., Ruenwongsa, P., and Panijpan, B. 2010. Hand-held models of ordered DNA and protein structures as 3D supplements to enhance student learning of helical biopolymers. Biochem. Mol. Biol. Edu. 38: 359–364. Jones, O. A. and Spencer, M. J. S. 2018. A simplified method for the 3D printing of molecular models for chemical education. J. Chem. Edu. 95: 88–96. Kaliakin, D. S., Zaari, R. R., and Varganov, S. A. 2015. 3D printed potential and free energy surfaces for teaching fundamental concepts in physical chemistry. J. Chem. Educ. 92: 2106–2112. Kaminsky, W. 2000. Wintensor: Ein WIN95/98/NT programm zum Darstellen tensorieller Eigenschaften. Z. Kristallogr. Suppl. 17: 51. Kaminsky, W. 2005. WinXMorph: A computer program to draw crystal morphology growth sectors and cross sections with export files in VRML V2.0 utf8-virtual reality format. J. Appl. Cryst. 38: 566–567. Kaminsky, W. 2007. From CIF to virtual morphology using the WinXMorph program. J. Appl. Cryst. 40: 382–385. Kaminsky, W., Snyder, T., Stone-Sundberg, J., et al. 2014. One-click preparation of 3D print files (*.stl *.wrl) from *.cif (crystallographic information framework) data using Cif2VRML. Powder Diffr. 29: S42–S47. Kaminsky, W., Snyder, T., Stone-Sundberg, J., et al. 2015. 3D printing of representation surfaces from tensor data of KH2 PO4 and low-quartz utilizing the WinTensor software. Z. Kristallogr. Cryst. Mater. 230: 651–656. Kat Cooper, A. and Oliver-Hoyo, M. T. 2017. Creating 3D physical models to probe student understanding of macromolecular structure. Biochem. Mol. Bio. Educ. 45: 491–500. Kitson, P., Macdonell, A., Tsuda, S., et al. 2014. Bringing crystal structures to reality by three-dimensional printing. Cryst. Growth Des 14: 2720–2724.

14-20 Lolur, P. and Dawes, R. 2014. 3D printing of molecular potential energy surface models. J. Chem. Educ. 91: 1181–1184. Macrae, C. F., Bruno, I. J., Chisholm, J. A., et al. 2008. Mercury CSD 2.0: New features for the visualisation and investigation of crystal structures. J. Appl. Crystallogr. 41: 466–470. Macrae, C. F., Sovago, I., Cottrell, S. J. et al. 2020. Mercury 4.0: From visualization to analysis, design and prediction. J. Appl. Cryst. 53: 226–235. Meyer, S. C. 2015. 3D printing of protein models in an undergraduate laboratory: Leucine zippers. J. Chem. Educ. 92: 2120–2125. Moeck, P. 2004. http://nanocrystallography.reserach.pdx. edu, assessed May 14, 2018. Moeck, P. 2006. www.nanocrystallography.org, assessed May 14, 2018. Moeck, P. 2016. http://nanocrystallography.reserach.pdx. edu/3D-print-files/, assessed May 14, 2018. Moeck, P. 2017. http://nanocrystallography.research.pdx. edu/3d-print-files/convert/, assessed May 14, 2018. Moeck, P. and Snyder T. 2018. Preparing 3D print files for nano-tech/science education from entries of large openaccess crystallographic databases at dedicated websites. Proc. 2018 IEEE 13th Nanotechnology and Devices Conference, October 14-17, 2018, Portland, OR, doi: 10.1109/NMDC.2018.8605880, Moeck, P., Čertík, O., Seipel, B., et al. 2005. Crystal structure visualizations in three dimensions with database support. Mater. Res. Soc. Symp. Proc. 909E: 0909-PP0305.1-6. Moeck, P., Čertík, O., Upreti, G., et al. 2006a. Crystal structure visualizations in three dimensions with support from the open access nano-crystallography database. J. Mater. Educ. 28: 87–95. Moeck, P., DeStefano, P., Cheung, I., et al. 2017. Straightforward routes from CIFs to three-dimensional printed crystallographic models. Acta Cryst. A 73: C1132. Moeck, P., Kaminsky, W., Fuentes-Cobas, L., et al. 2016. 3D printed models of materials tensor representations and the crystal morphology of alpha quartz. Symmetry Culture Sci. 27: 319–330. Moeck, P., Seipel, B., Upreti, G., et al. 2006b. Freely accessible internet resources for nanoscience and nanotechnology education and research at Portland State University’s research servers. Mater. Res. Soc. Symp. Proc. 931: 0931-KK01-04. Moeck, P., Kaminsky, W., and Snyder, T. J. 2014a. Presentation and answers to a few questions about 3D printing of crystallographic models. Int. Union Crystallogr. Newslett. 22: 7–9. Moeck, P., Stone-Sundberg, J., Snyder, T., et al. 2014b. Crystallography in interdisciplinary college education settings: Educational offsprings of the crystallography open database and 3D printed crystallographic models. In: Educating and Mentoring Young Materials Scientists

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15 Dissemination, Outreach, and Training on Nanoscience and Nanotechnology ´ hez Joaquin Tutor-Sanc Thermal Chain Technology Advanced Products Network ETSI-ICAI Universidad Pontificia Comillas

David Quesada Miami Dade College, Wolfson Campus

Javier Gamo-Aranda St. Louis University Madrid Campus

Noboru Takeuchi Centro de Nanociencia y Nanotecnologia, UNAM

Angela Camacho Universidad de los Andes

Jordi Diaz Universidad de Barcelona

Fernanda Pilaquinga Pontificia Universidad Catolica del Ecuador, Ecuador and University of Balearic Islands

Eliza Jara Pontificia Universidad Catolica del Ecuador, Ecuador

Rainer Christoph Universidad Francisco Gavidia

Diana Padilla Rueda Universidad del Atlantico

15.1

15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15-1 15.2 What Is the Importance of Dissemination, Outreach, and Training in Nanoscience and Nanotechnology? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15-2 Scientific and Technological Reasons Social Motivations

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Commercial and Business Reasons

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15.3 Scientific and Technological Dissemination and Training . . . . . . . . . . . . . . . . 15.4 How Is Dissemination, Outreach, and Training in Nanoscience and Nanotechnology Being Achieved through NANODYF? . . . . . . . . . . . . . . . . . . 15.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction

In 1959, the American physicist Richard Feynman claimed, “. . . There is plenty of room at the bottom. . . ”, opening the discussion for the importance “of manipulating and controlling things at small scales”. The interest in such topics started to flourish in many countries and regions around the world from the moment the Japanese scientist Norio Taniguchi introduced the term “Nanotechnology” in 1974. A further increase in exchange of information among scientists and innovators occurred after the visionary ideas of Eric Drexler, in his book “Engines of Creation”, and the discovery of the scanning tunneling microscope in 1981 by

15-4 15-6 15-7 15-8

a team of researchers from IBM Ruschlikon, Switzerland, led by Heinrich Rhorer (History of Nanotechnology 2018, Hulla et al. 2015, Nanotechnology Timeline 2018). Since the second half of the 1980s, national and regional strategies were defined and executed to introduce and diversify these emergent technologies. Moreover, learning outcomes of natural science courses from elementary and secondary levels to higher education for decades have included formalized skill-promoting activities oriented toward the concepts and generalities of Nanosciences and Nanotechnologies. Such experiences have facilitated the foundation of a certain culture “Nano” across different sectors of the population. In this sense, there is a general consensus that an adequate

15-1

15-2 understanding of both, scientific and technological dissemination and training, is necessary for developing consistent and reliable strategies for the dissemination and training in the areas of Nanosciences and Nanotechnologies. Likewise, the digital age is calling for a disruptive approach in education, where boards of governors must be trained also on such disruptions in order to adequately address new challenges. Despite their relevance, these emergent technologies are not understood by and even known to wide sectors of the population (Ahmed 2012, Chari et al. 2012, Ernst 2009, Ghattas and Carver 2012, Gomez-Ferri 2012, Srinivas 2014). Other important topics, such as global warming and climate changes, biotechnology and biomedical engineering, quantum computing and cyber-security, social and business networks, neurosciences, and cancer, are also competing for attention and insertion into educational and outreach efforts. Taking into account the limited number of credits needed for graduation, it is also a matter of scheduling for educational decision-makers because foundational and general education courses must be complemented with electives focusing on all new emerging topics. Noteworthy, for the last decade, the increasing interest in data science and machine learning as new approaches to address old problems in science and technology also competes for attention with Nanosciences and Nanotechnologies (Beck et al. 2016, Panchal et al. 2013, Stanton 2016). This situation is not only associated with “nano-related” topics, it is also occurring across the board and it has been a matter of analysis by educators and educational policy decision-makers (García-Guerrero and Foladori 2015, Meyyappan 2004, Sánchez-Mora and Taguena-Parga 2011). In recent years, educators and policy-makers have moved toward a more comprehensive integration of Nanosciences and Nanotechnologies with other emergent technologies and topics of interests, such as biotechnology, environmental remediation, quantum computing, and data-driven design of new materials (materials informatics). Such integration will demand from instructors, educational policy-makers, and the industrial sector the elaboration of new educational strategies to make citizens aware of these fast-changing fields and their potential impacts on the future of modern societies. The feasibility of this idea is backed-up by the amount of human resources created since 1980, when nanotechnology was established in the technological arena. Additionally, there are a considerable number of research-oriented and serving-learning institutions that have been created since then and which include Nanosciences as one of the prioritized topics in their research and development programs. Many of these initiatives have already transformed into technological parks and research corporations, which serve also as incubators for leaders and “Nano-oriented” start-ups. Motivated by the above-mentioned challenges, this communication is aimed at analyzing strategies for further dissemination of Nanosciences and Nanotechnologies, describing some of the experiences already accumulated in Ibero-America and the USA, and suggesting implementation methodologies that take into account the cultural,

Public Policy, Education, and Global Trends economic, and political differences. Such an abundant diversity in approaches constitutes a pool of possibilities with inter-breeding potential that might facilitate the introduction and fast dissemination of modern technologies.

15.2

What Is the Importance of Dissemination, Outreach, and Training in Nanoscience and Nanotechnology?

The answer to the question “Who needs a good scientific education?” might seem very silly, and the answer is “Everyone!” A good scientific training is important for all, including both STEM-oriented people and non-technical ones. However, it is also important that funding agencies and institutions create initiatives to encourage interdisciplinary research that can be more easily translated into community initiatives to enhance nanotechnology knowledge and, therefore, overcome the existing knowledge gap. The social awareness about the benefits of science and technology, and its methods will determine the progress of a country as well as the willingness to assign resources for further developments. Likewise, it is also important to choose the right dose and the proper content to promote among different professions and groups of the population, such that they might identify the utility of learning new subjects.

15.2.1

Scientific and Technological Reasons

Scientific and technological reasons are associated with the potential applications, new knowledge to be acquired, and the optimization of existing technologies. It is worth noticing that, during the process of growth and design of nanostructures, it is possible to control and optimize the fundamental physical and chemical properties of the materials involved. The large variety of compounds, physical properties, and chemical compositions require the use of statistical (data mining, clustering, machine learning, deep learning, and neural networks) and optimization methods (non-linear optimization, tensor flow, and genetic algorithms) in order to classify and design new materials, as for example, topological insulators and other topological phases of matter. Thus, using this potential, high-performance products and technologies might be created and/or optimized as it has never occurred before. Artificially created nanosystems emulating the organizational principles of naturally occurring biological systems (biomimicry) are opening opportunities in rehabilitation, therapeutics, and diagnosis of medical conditions, as for example, molecular machines and nanobots already used in Nanotheranostics (a term coined to represent Therapy and Diagnosis based on Nanocarriers), tissue engineering, and nanoprosthetics. Nanotechnology will allow locating components inside living cells and obtain new materials following Nature’s own self-organization principles. The powerful combination of materials science and biotechnology will allow the germination of completely novel processes

Dissemination, Outreach and Training on Nano and industries. Biomedical engineering and tissue engineering will be two areas that are likely to grow and benefit from Nanosciences, setting the foundations for personalized regenerative medicine. Nanostructured systems such as nanoparticles and nanolayers have a very high surface-to-volume ratio, which make them ideal for designing nanocomposites, for controlling chemical reactions and drug release, and for improving and enhancing energy storage capabilities. Nanostructures are so small that they can be used to build systems that contain a greater density of components compared to micrometric objects. By controlling the interactions and complexity of the nanostructures, new concepts of electronic devices can be achieved, smaller and faster circuits, with more sophisticated functions, and a great reduction of power consumption. Nanostructured materials are known to be the building blocks of quantum computers, new wearable device technologies capable to track physiological signatures. The energetic and environmental sectors will benefit heavily from nanostructures (Christoph et al. 2019, Diallo et al. 2013). It is well documented that quantum dot solar cells might enhance the conversion ratio and increase the number of photo-electrons, busting the photovoltaic industry and reducing the use of fossil fuels. An optimized photovoltaic industry might also boost the creation of new job opportunities and a decentralization of the large-scale energy production. However, the use of nanoparticles in agriculture is reducing the adverse effects of droughts, pesticides, and bugs outbreaks, leading to a more environmentfriendly agriculture.

15.2.2

Commercial and Business Reasons

Products related to Nanotechnology are continuously mentioned among technologists, people in charge of commercialization, and promoters in the business-industrial environment. In support of the aforementioned claim, the number of publications about Nanoscience and Nanotechnology (with a prefix Nano within the title) has increased from 1999 AND PUBYEAR < 2018.

17.5 17.5.1

Data Analysis Quantum of Literature in Nanobiotechnology vs Year

During 1998–2017, 273,581 articles appeared in nanobiotechnology, with an average publication of 13,679 per year. The output of nanobiotechnology in this field increased from 731 papers in the year 1998 to 34,411 papers in the year 2017. However publications increasing every year can be seen for the study period. The quantum of literature in nanobiotechnology that is included in Scopus database year wise has been shown in Table 17.1.

17.5.2

Language-Wise Distribution of Publications

The scholarly communication is effective through English language in almost all the countries irrespective of the native language. This phenomenon is not an exception to the subject of nanobiotechnology. Maximum total research output, 97.16%, were only in English. This is followed by Chinese (1.71%) and Japanese (0.24%) language (Table 17.2).

TABLE 17.1 Quantum of Literature in Nanobiotechnology vs Year Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

No. of Publications 731 901 1,065 1,532 2,335 3,113 4,771 6,761 7,994 9,461 12,664 14,598 16,937 19,824 21,876 25,145 27,731 30,209 31,522 34,411 273,581

Percentage 0.27 0.33 0.39 0.56 0.85 1.14 1.74 2.47 2.92 3.46 4.63 5.34 6.19 7.25 8.00 9.19 10.14 11.04 11.52 12.58 100.00

Scientometric Assessment TABLE 17.2 Publications Sl. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

17.5.3

17-3

Language-Wise Distribution of

Language English Chinese Japanese Russian French German Spanish Korean Portuguese Polish Persian Czech Italian Turkish Ukrainian Croatian Slovenian Hungarian Romanian Serbian Slovak Bosnian Bulgarian Finnish Greek Arabic Dutch Swedish Estonian Indonesian Lithuanian Malay Danish Latvian Hebrew Thai Total

Total Publications 265,812 4,672 666 458 362 327 283 168 150 144 89 82 69 61 59 31 24 23 21 12 12 6 6 6 6 5 5 4 3 3 3 3 2 2 1 1 273,581

17.5.4

Percentage 97.16 1.71 0.24 0.17 0.13 0.12 0.10 0.06 0.05 0.05 0.03 0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100

Bibliographic Form of Nanobiotechnology Publications

It is observed that the scientific research output is largely published in subject periodicals and sometimes as conference proceedings. Of course, some of those papers presented in conferences are further updated and published in journals of the respective branch of knowledge. In this study, it is observed from Table 17.3 that 72.93% are journal articles and 12.20% were conference papers. By and large, it is found that the scholarly communication of nanobiotechnology research output is mostly through journals and conferences. The bibliography covered 273,581 items. These comprised 199,534 journal articles, 33,379 conference papers, 6,587 book chapters, etc.

TABLE 17.3 Publications

Bibliographic Form of Nanobiotechnology

Bibliographic Form Article Conference paper Review Book chapter Conference review Editorial Book Short survey Note Article in press Erratum Letter Retracted Business article

Total Publications 199,534 33,379 24,950 6,587 2,461 1,698 1,265 1,164 994 714 524 300 6 5

Percentage 72.93 12.20 9.12 2.41 0.90 0.62 0.46 0.43 0.36 0.26 0.19 0.11 0.00 0.00

Scientometrics Indicators and Statistical Techniques Employed

The following bibliometric/scientometrics indicators and statistical techniques were employed while analyzing the data on nanobiotechnology research output collected from the Scopus databases: • • • • • • • •

Relative Growth Rate (RGR) Doubling Time (Dt) Authorship Pattern Collaboration Index (CI) Degree of Collaboration (DC) Co-authorship Index (CAI) Activity Index (AI) Relative Quality Index (RQI)

Relative Growth Rate (RGR) and Doubling Time (Dt) The growth of nanobiotechnology publications was analyzed by RGR and Dt. RGR is a measure to study the increase in number of articles of time (Mahapatra 1985) and the Dt is directly related to RGR. The mean RGR over the specific period of interval can be calculated from the following equation: loge2 W − loge1 W (17.1) 1 − 2R = 2T − 1T where 1 − 2R = mean RGR over the specific period of interval loge1 W = log of initial number of articles/pages loge2 W = log of final number of articles/pages after a specific period of interval 2T − 1T = the unit difference between the initial time and the final time. The year can be taken here as the unit of time. There exists a direct equivalence between the RGR and the Dt. If natural logarithm is used this difference has a value of 0.693. Thus the corresponding Dt for each specific period of interval and for articles can be calculated by the following formula: Doubling time (Dt) =

0.693 R

(17.2)

RGR and Dt have been administrated to nanobiotechnology literature. The chronological distribution, RGR, Dt, and publications in the field of nanobiotechnology during the period 1998–2017 has been shown in Table 17.4. When the RGR is constant, the quantity undergoes exponential growth and has a constant Dt or period which can be calculated directly from the growth rate. Further it can be seen from Figure 17.1 that there is a decrease in RGR during the period of study. The Dt has also shown an increasing trend when calculated year-wise. The Dt was 0.1 in 1998 and increased to 4.97 in 2017. Since then there exist continuous increase trends during the study period.

17-4

Public Policy, Education, and Global Trends

TABLE 17.4 Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

Relative Growth Rate and Doubling Time

No. of Publications 731 901 1,065 1,532 2,335 3,113 4,771 6,761 7,994 9,461 12,664 14,598 16,937 19,824 21,876 25,145 27,731 30,209 31,522 34,411 273,581

Cum. Publications 731 1,632 2,697 4,229 6,564 9,677 14,448 21,209 29,203 38,664 51,328 65,926 82,863 102,687 124,563 149,708 177,439 207,648 239,170 273,581

W1 0.00 6.59 7.40 7.90 8.35 8.79 9.18 9.58 9.96 10.28 10.56 10.85 11.10 11.32 11.54 11.73 11.92 12.09 12.24 12.38

W2 6.59 7.40 7.90 8.35 8.79 9.18 9.58 9.96 10.28 10.56 10.85 11.10 11.32 11.54 11.73 11.92 12.09 12.24 12.38 12.52

RGR – 0.80 0.50 0.45 0.44 0.39 0.40 0.38 0.32 0.28 0.28 0.25 0.23 0.21 0.19 0.18 0.17 0.15 0.14 0.14

Dt 0.11 0.86 1.38 1.54 1.58 1.79 1.73 1.81 2.17 2.47 2.45 2.77 3.03 3.23 3.59 3.77 4.08 4.51 4.78 4.97

The formula of collaboration is given by Lawan (1980) as follows: A j=1 jfj CI = (17.3) N It is a measure of mean number of authors. Although it is easily computable it is not easily interpretable as a degree, for it has no upper limit. Moreover it gives a non-zero weight to single-authored papers, which involve no collaboration.

Subramanyam’s (1983) formula has been adopted to examine the extent of research collaboration in the study.

6 RGR and Dt

Collaboration Index (CI)

Degree of Collaboration (DC)

Relative Growth Rate and Doubling Time

7

collaboration should have a value between 0 and 1, with 0 corresponding to single-authored papers, and 1 for the case where all papers are maximally authored, i.e. every publication in the collection has all authors in the collection as co-authors.

5

C = Nm /Nm + Ns

4 3

RGR

2

Dt

0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

1

Year

FIGURE 17.1

Relative growth rate and doubling time.

Authorship Pattern and Collaboration To show the trend toward multiple authorships in a discipline, many studies have used either the mean number of authors per paper, termed the CI by Lawani (1980), or the proportion of multiple-authored papers, called DC by Subramanyam (1983), as a measure of the strength of collaboration in a discipline. Assuming that these two measures seems to be inadequate, Ajiferuke et al. (1988) derived a single measure that incorporates some of the merits of both of the above. Ideally, it is desired that a quantification of TABLE 17.5 Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

(17.4)

where C = DC in a subject N m = number of multiple-authored papers N s = number of single-authored papers The year-wise distribution of nanobiotechnology contributions according to the number of authors is shown in Table 17.5. It is evident from Table 17.5 that nearly 94% were collaborative research either by two authors or more. Only 6% were by single authors. The DC ranges between 0.82 and 0.96. This indicates there exists collaborative research in nanobiotechnology. Collaborative index ranges between 3.20 and 5.49. Contributions and Impact of Most Productive Authors in Nanobiotechnology Ten authors have been identified as most productive, who have published 188 and more papers in nanobiotechnology research. The publication profiles of these 10 authors along with their research output, citations received, and

Authorship Pattern and Author Collaboration

Single Author 115 102 169 238 396 552 772 1,008 995 1,020 1,107 1,156 1,312 1,309 1,335 1,439 1,399 1,221 1,247 127 17,019

Two Authors 117 156 159 236 352 456 710 974 1,171 1,311 1,736 1,880 2,209 2,441 2,578 2,915 3,042 3,179 3,075 281 28,978

Three Authors 121 160 158 267 373 467 811 1,167 1,305 1,523 2,153 2,339 2,681 3,078 3,171 3,613 3,857 4,345 4,125 387 36,101

More than Three Authors 378 483 579 800 1,214 1,638 2,478 3,612 4,523 5,607 7,668 9,223 10,735 12,996 14,792 17,178 19,433 21,464 23,075 33,616 191,492

Total 731 901 1,065 1,532 2,335 3,113 4,771 6,761 7,994 9,461 12,664 14,598 16,937 19,824 21,876 25,145 27,731 30,209 31,522 34,411 273,581

DC 0.84 0.89 0.84 0.85 0.83 0.82 0.84 0.85 0.88 0.89 0.91 0.92 0.92 0.93 0.94 0.94 0.95 0.96 0.96 0.99 0.94

Total No. of Authors 3,035 3,790 4,510 6,278 9,693 12,674 19,495 28,322 34,492 42,144 57,722 75,300 80,703 97,013 103,042 128,987 146,279 173,571 100,937 189,084 131,7071

CI 4.15 4.21 4.23 4.07 4.15 4.07 4.09 4.19 4.31 4.45 4.56 5.16 4.76 4.89 4.71 5.13 5.27 5.75 3.20 5.49 4.81

Scientometric Assessment TABLE 17.6 Top 10 Authors Webster, T.J. Yuan, R. Ramakrishna, S. Chai, Y.

Jiang, L.

Tan, W.

Ferrari, M. Ju, H. Boccaccini, A.R. Hashim, U.

17-5

Most Prolific Authors in the Field of Nanobiotechnology Affiliation Department of Chemical Engineering, Northeastern University, Boston, MA, United States Key Laboratory of Luminescence and Real-Time Analytic Chemistry, Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, China Department of Mechanical Engineering, National University of Singapore, 2 Engineering Drive 3, Singapore, Singapore Key Laboratory of Luminescent and Real-Time Analytical Chemistry (Southwest University), Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, China Key Laboratory of Bio-inspired Smart Interfacial Science and Technology, Ministry of Education, School of Chemistry, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Life Sciences, Hunan University, Changsha Hunan, China Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China Department of Materials Science and Engineering, Institute of Biomaterials, University of Erlangen-Nuremberg, Erlangen, Germany Institute of Nano Electronic Engineering, University Malaysia Perlis, Perlis, Kangar, Malaysia

TP 433

TC 16,094

CPP 37

Solo Author 11

Country USA

353

10,105

29

0

China

337

25,782

77

0

Singapore

265

8,176

31

0

China

256

16,295

64

1

China

244

18,822

77

2

China

209

12,428

59

8

USA

196

10,991

56

1

China

190

5,537

29

0

Germany

188

896

5

1

Malaysia

TP - Total Publications; TC - Total Citations; CPP - Citation Per Paper

h-index values are presented in Table 17.6. These 10 authors together contributed 2,671 papers with an average of 267 papers per author and account for 0.98% share in the cumulative global publications output during 1998– 2017. Considering the quality/impact of papers, these 10 productive authors have received a total of 125,126 citations for 10 papers with an average of 47 citations per paper. Five authors have registered higher impact than the average impact compared with the other papers. These are Ramakrishna, S. and Tan, W. with 77 citations per paper, followed by Jiang with 64 CPP, Ferrari, M. with 59 CPP, and Ju, H. with 56 citations (Table 17.6). Among the top 10 authors, China’s contribution is the highest one and followed by United States, Germany, Singapore, and Malaysia. Co-authorship Index (CAI) The study of how the pattern of co-authorship and the use of CAI suggested by Garg and Padhi (2002) have been made TABLE 17.7 Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

is explained below. For calculating CAI the entire dataset was divided into five blocks. CAI = (Nij /Nio ) / (Noj /Noo ) × 100

(17.5)

where N ij = number of papers having j authors in block i N io = total output of block i N oj = number of papers having j authors for all blocks N oo = total number of papers for all authors and all blocks It can be seen from Table 17.7 that single author during 1998–2011 are higher than the average of co-authorship effort. Similarly, the two authors during 1998–2014 year and the three authors during 2014–2017 are higher than the average of co-authorship effort. It can be concluded from Table 17.7 that collaborative research persists over the years, and it has a fluctuation from 1998 onward. It can be derived

Co-authorship Index (CAI)

Single Author 115 102 169 238 396 552 772 1,008 995 1,020 1,107 1,156 1,312 1,309 1,335 1,439 1,399 1,221 1,247 127 17,019

CAI 252.8904 181.9815 255.0874 249.7296 272.6216 285.0440 260.1116 239.6633 200.0833 173.3066 140.5169 127.2965 124.5229 106.1452 98.09915 91.99433 81.09687 64.97274 63.59231 5.932781 100

Two Authors 117 156 159 236 352 456 710 974 1,171 1,311 1,736 1,880 2,209 2,441 2,578 2,915 3,042 3,179 3,075 281 28,978

CAI 151.1075 163.4622 140.9500 145.4356 142.3224 138.2940 140.4968 136.0083 138.2962 130.8227 129.4185 121.5856 123.1336 116.2503 111.2583 109.4471 103.5646 99.3508 92.0977 7.7095 100

Three Authors 121 160 158 267 373 467 811 1,167 1,305 1,523 2,153 2,339 2,681 3,078 3,171 3,613 3,857 4,345 4,125 387 36,101

CAI 125.4396 134.5742 112.4279 132.0746 121.0566 113.6853 128.8185 130.8057 123.7124 121.9915 128.8368 121.4237 119.9574 117.6641 109.8487 108.8888 105.4025 108.9984 99.1692 8.52276 100

More than Three Authors 378 483 579 800 1,214 1,638 2,478 3,612 4,523 5,607 7,668 9,223 10,735 12,996 14,792 17,178 19,433 21,464 23,075 33,616 191,492

CAI 73.8770 76.5874 77.6719 74.6047 74.2791 75.1744 74.2039 76.3259 80.8346 84.6698 86.5060 90.2639 90.5525 93.6599 96.6038 97.6014 100.1174 101.5102 104.5835 139.5674 100

Total 731 901 1,065 1,532 2,335 3,113 4,771 6,761 7,994 9,461 12,664 14,598 16,937 19,824 21,876 25,145 27,731 30,209 31,522 34,411 273,581

17-6

Public Policy, Education, and Global Trends

from Table 17.7 that the CAI for single-author papers was 181.9815 in 1999 which enhanced to 285.0440 in 2003. Activity Index (AI) AI characterizes the relative research effort of a country to a given field and it is defined as AI =

Given field’s share in the country’s publication output Given field’s share in the world’s publication output × 100 (17.6)

In this study AI for most productive countries has been calculated for different years to see how nanobiotechnology research activity changed during different years using the formula. First suggested by Frame and used among others by Schubert and Braun (1986), de Solla Price (1981), and Karki and Garg (1997) AI characterizes the relative research effort of a country to a given field (Table 17.8). AI = (Nij /Nio ) / (Noj /Noo ) × 100

(17.7)

where N ij = number papers in theme i and block A N io = number papers in theme I for all blocks N oj = number of papers in all fields block A N oo = number of papers for all fields and all blocks Relative Quality Index (RQI) This indicator is a ratio of the proportion of high-quality papers (NHQ%) to the proportion of the publications (TNP%) suggested by Nagpaul (1985) and used by Garg and Padhi (2002) for intercomparison of quality. Number of High-Quality Papers (NHQ) Citation per paper for different countries and institutions was calculated as the pattern of citation varied from one country to another. Papers that received more than twice the average citations have been considered as high-quality papers. Number of high quality papers for a country/year Total number of quality papers (17.8) Number of high-quality papers for a country/year NHQ% =

TNP% =

Total publication output of a country/year Total publication out for all countries/years (17.9)

TABLE 17.8 Sl. No. 1 2 3 4 5 6 7 8 9 10

The measure relates to the incidence of high-quality papers for a country or an institution. A value of RQI > 1 indicates higher than average value, whereas a value of RQI < 1 indicates lower than average quality. Table 17.9 lists the study period 1998–2017 with their total number of publications, total citations, CPP, NHQ, and RQI. The average value of CPP is 24. The value of CPP is highest in 1999 due to the age of the publication and less than average during 2012–17. The standing of publications from the values of RQI indicates that years 1998–2014 have more than average incidence of high-quality papers, as the value of the RQI is more than 1 and, for the other years, the incidence of high-quality papers is less than average. International Collaboration of Most Prolific Countries The global publication share of the top 10 most productive countries in nanobiotechnology research varies for the period 1998–2017. The global publication share of the top 10 most productive countries in nanobiotechnology research varies from 3.14% to 25.56% during 1998–2017. The United States tops the list, with a share of 25.56% during 1998–2017. China ranks the second position with 20.94% share, followed by India (9.16%, third rank), Germany, United Kingdom, South Korea, Japan, France, Italy, and Spain rank from fourth to tenth position (Table 17.10). The United States holds the first position in International Collaboration with

TABLE 17.9 Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 TOTAL

Relative Quality Index

TP 731 901 1,065 1,532 2,335 3,113 4,771 6,761 7,994 9,461 12,664 14,598 16,937 19,824 21,876 25,145 27,731 30,209 31,522 34,411 27,3581

TC 65,082 60,412 90,695 132,106 183,306 257,377 337,844 419,559 448,235 474,074 585,422 601,594 638,999 602,189 517,850 498,485 387,864 278,312 119,040 78,933 677,7378

CPP 89.03 67.05 85.16 86.23 78.50 82.68 70.81 62.06 56.07 50.11 46.23 41.21 37.73 30.38 23.67 19.82 13.99 9.21 3.78 2.29 24.64

NHQ – – – 1 1 1 2 3 3 4 5 5 6 8 8 10 10 11 11 11

Activity Index of Top 10 Countries

Top 10 Countries USA China India Germany United Kingdom South Korea Japan France Italy Spain World Contribution

1998–2002 2,452 (146.13) 228 (16.59) 85 (18.07) 707 (171.59) 442 (137.53) 98 (31.07) 415 (134.89) 483 (173.41) 274 (103.06) 145 (70.19) 6,564

No. of Publications (Activity Index) 2003–07 2008–12 2013–17 11,424 (139.21) 24,375 (111.00) 31,687 (83.18) 3,458 (51.44) 15,679 (71.34) 37,932 (121.54) 725 (31.52) 4,776 (77.60) 14,016 (131.27) 2,614 (129.73) 5,681 (105.36) 8,171 (87.35) 1,827 (116.25) 4,465 (106.16) 6,661 (91.29) 1,214 (78.71) 4,313 (104.51) 7,519 (105.02) 2,423 (161.04) 4,463 (110.85) 5,522 (79.06) 1,551 (113.87) 3,833 (105.16) 5,742 (90.81) 1,142 (87.84) 3,348 (96.23) 6,317 (104.66) 718 (71.07) 2,702 (99.95) 5,045 (107.57) 32,100 85,899 149,018

Total 69,938 57,297 19,602 17,173 13,395 13,144 12,823 11,609 11,081 8,610 273,581

RQI – – – 1.05 1.05 1.05 1.05 1.04 1.04 1.04 1.04 1.03 1.04 1.04 1.04 1.04 1.02 0.99 0.92 0.89

Scientometric Assessment

17-7

TABLE 17.10 International Collaboration of Most Prolific Countries Countries USA China India Germany United Kingdom South Korea Japan France Italy Spain Total

TP 69,936 57,297 19,602 17,173 13,395 13,144 12,823 11,609 11,081 8,610 273,581

ICP 26,739 14,171 4,840 10,222 8,336 4,582 4,390 6,879 5,414 4,976

ICP (%) 38.23 24.73 24.69 59.52 62.23 34.86 34.24 59.26 48.86 57.79

other countries. Percentage of ICP of United Kingdom holds the first position based on the productivity of research publication during the study period. International Collaboration of publications of the United States has been visualized by using Pajek software (Figure 17.2) contribution of the 100 collaborated countries along with their publications in brackets were represented in Figure 17.2.

17.6

Scientometrics Indices

To increase the scientometric studies different measures and indices have been developed and their definition/formulas are presented in Table 17.11. Taking a fixed number or a certain percentage of all publications into consideration would mean a somewhat arbitrary and biased choice. To solve this problem Hirsh (2005) introduced h-index.

FIGURE 17.2

TABLE 17.11

Scientometrics Indices

Index h

Introducer Hirsch

Years 2005

g

Leo Egghe

2006

hg

Alonso

2010

p

Gangan Prathap

2010

Definition/formula A scientist has index h if h of his/her Np papers have at least h citations each, and the other (Np – h) papers have no more than h citations each The highest number g of papers that together received g 2 or more citations. From this definition it is already clear that g ≥ h hg =

hg

p = hm =

 C 2  13 P

The h-index is not an average, a percentile, or a fraction; it is a totally a new way of measuring performance impact, visibility, quality, etc. of the career of a scientist. It is a simple measure without any threshold. Based on this h-index various indices are developed for evaluating the career of individual scientists according to their scientific output. The g-index (Egghe 2006) is an h-type index for quantifying the scientific productivity of physicists and other scientists based on their publication record. h- and g-Indices describe the most productive core of the output of a researcher and inform about the number of papers in the core. Moreover the online database such as Web of Science, Scopus, Google Scholar provides the hindex.

International collaboration of United States: Visualization by using Pajek.

17-8

Public Policy, Education, and Global Trends

Prathap (2011) proposed an index called p-index. The p-index gives the best balance between quality (C/P) and quantity (C).

17.6.1

Properties of Indices

• Property h < hg < g indicates more citation. • p-Index much larger than h and g indices indicates the existence of uncitedness of papers. The top 10 most productive institutions involved in nanobiotechnology research have published more than 1,000 papers during 1998–2017. The publication profiles of these 10 institutions along with their research output, citations received, and h-index values are presented in Table 17.12. These 10 institutions involved in nanobiotechnology research together have contributed 13.14% shares (with 35,973 papers) in the cumulative publications output during 1998–2017. Four institutions from China actively produce research articles in the field of nanobiotechnology. Massachusetts Institute of Technology from United States is high in h-index, g-index, hg-index, and p-index. Seoul National University from South Korea has produced 1,795 publication holds the 10th rank. h-Index of the Seoul National University is higher than Shanghai Jiao Tong University (seventh rank), Zhejiang University (sixth rank), and Consiglio Nazionale delle Ricerche (fourth rank). The contributions of the most productive institutions and the citation, average citation, international collaboration, h-index, g-index, hg-index, and p-index have been analyzed, as shown in Table 17.12. Based on h-index (Hirsch 2005) productivity and impact of the published work of the institutions were analyzed (Table 17.12). The quantification of the productivity of the institutions and based on their publication record were identified by using g-index (Egghe 2006) technique. g-Index is more sensitive than h-index in the assessment of selective scientists, since this type of scientist shows in average a higher g/h-index ratio and a better position in g-index rankings than in the h-index ones (Radrigo and Maria 2008). Massachusetts Institute of Technology (334). Based on the results it is found that g-index is always higher or equal to h-index (Egghe 2006). Geometric mean of h and g indices is hg-index (Alonso et al. 2010). hg-Index value is nearer to h than to g., i.e. h < hg < g and hg–h < g–hg. The hg-index provides gain to compare scientists. hgIndex is used to balance the impact of the majority of the

TABLE 17.12

best publications of the author and very highly cited ones, that is it reduces the impact of single very high cited publications. p-Index is the performance index balanced between activity and excellence (Prathap and Gupta 2009). The p-index provides the best balance between quantity and quality (Prathap 2010). The p-index represents a combination of size and quality and it would be ideal to compare institutions and countries on this index. Hence the institutions were compared by using p-index.

17.6.2

Highly Cited Publications in the Field of Nanobiotechnology during 1998–2017

An article published in 2004 received 9,773 citations and holds the first rank based on the citations received. The quality of the publication is evaluated by citation analysis. The characteristics of highly cited papers (the 1% most highly cited papers) are listed in Table 17.13 among the papers related to nanobiotechnology research during 1998–2017. Citations received by the 10 top-cited papers accumulated to 1,503 (12%). All the 10 papers are published by two are more authors. Top two highly cited papers were produced by the authors of France, six papers from United States including one from United States and Germany, and two papers from Japan.

17.6.3

Impact of Journals

Impact of journals in the field of nanobiotechnology with more productive articles is shown in Table 17.14. The impact factor is one of these; it is a measure of the frequency with which the “average article” in a journal has been cited in a given period of time. Based on the average citations per paper the Biomaterials holds the first position (82.06), followed by ACS Nano (62.06), Analytical Chemistry (54.79), Langmuir (40.75), etc.

17.7

Conclusion

Due to technological importance and expected economic activity, nanobiotechnology has been intensively investigated by scientometric methods. In this chapter the current status of nanobiotechnology has been presented. Initially frequency and percentile method have been evolved chronologically. The progress has further been measured using

Scientometric Indices of Most Productive Institutions

Top 10 Institutions Chinese Academy of Science Ministry of Education China CNRS Centre National de la Recherche Scientifique Consiglio Nazionale delle Ricerche Massachusetts Institute of Technology National University of Singapore Zhejiang University Shanghai Jiao Tong University Nanyang Technological University Seoul National University

Country China China France Italy United States Singapore China China Singapore South Korea

TP 9,043 8,184 4,303 2,333 2,289 2,268 1,937 1,915 1,906 1,795

TC 296,912 174,542 140,320 47,871 244,701 12,2707 53,470 47,018 65,083 60,397

h-Index 208 137 149 88 215 168 99 96 116 114

g-Index 312 197 258 136 334 276 171 142 190 189

hg-Index 254.7469 164.2833 196.0663 109.3984 267.9739 215.3323 130.1115 116.7562 148.4587 146.7856

p-Index 213.6228 154.9809 166.0183 99.40543 296.8533 187.9455 113.858 104.9027 130.4981 126.6646

Armand M., Tarascon J.-M.

Kitagawa S., Kitaura R., Noro S.-I.

Bruchez Jr. M., Moronne M., Gin P., Weiss S., Alivisatos A.P.

Chan W.C.W., Nie S.

Michalet X., Pinaud F.F., Bentolila L.A., Tsay J.M., Doose S., Li J.J., Sundaresan G., Wu A.M., Gambhir S.S., Weiss S.

Love J.C., Estroff L.A., Kriebel J.K., Nuzzo R.G., Whitesides G.M.

Nel A., Xia T., Mädler L., Li N.

Sinha Ray S., Okamoto M.

Cui Y., Wei Q., Park H., Lieber C.M.

2

3

4

5

6

7

8

9

10

Highly Cited Articles

Authors Daniel M.-C., Astruc D.

Sl. No 1

TABLE 17.13

Polymer/layered silicate nanocomposites: A review from preparation to processing Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species

2001

2003

2006

Toxic potential of materials at the nanolevel

4,682

5,007

5,136

5,435

5694

2005

2005

6,275

7,163

7439

7,606

Citation 9,223

1998

1998

2004

2008

Years 2004

Self-assembled monolayers of thiolates on metals as a form of nanotechnology

Quantum dot bioconjugates for ultrasensitive nonisotopic detection Quantum dots for live cells, in vivo imaging, and diagnostics

Semiconductor nanocrystals as fluorescent biological labels

Functional porous coordination polymers

Title Gold nanoparticles: Assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology Building better batteries

Dept. of Chemistry and Biochemistry, University of California, 607 Charles E. Young Drive East, Los Angeles, CA 90095, United States; Crump Inst. for Molecular Imaging, Dept. of Molec. and Med. Pharmacol., University of California, 700 Westwood Plaza, Los Angeles, CA 90095, United States; Department of Physiology, David Geffen School of Medicine, University of California, 700 Westwood Plaza, Los Angeles, CA 90095, United States; Dept. of Radiology and Bio-X Program, Molec. Imaging Prog. Stanford (MIPS), Stanford Univ. School of Medicine, Stanford, CA 94305, United States; Angew. Department of Chemistry, Fredrick Seitz Mat. Res. Laboratory, Univ. of Illinois-Urbana-Champaign, Urbana, IL 61801, United States; Dept. of Chem. and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, United States Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States; California NANOSystems Institute, University of California, Los Angeles, CA 90095, United States; Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, United States Adv. Polymeric Materials Engineering, Graduate School of Engineering, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku, Nagoya 468 8511, Japan Department of Chemistry, Harvard University, Cambridge, MA 02138, United States

LRCS, CNRS UMR-6007, Université de Picardie Jules Verne, Amiens, France Dept. of Synth. Chem./Biol. Chem., Graduate School of Engineering, Kyoto University, Nisikyo-ku, Kyoto 615-8510, Japan; Toyota Ctrl. R and D Lab. Inc., Nagakute, Aichi, 480-1192, Japan; Supramolecular Science Laboratory, RIKEN, Inst. of Phys. and Chemical Research, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan Department of Chemistry, University of California, Berkeley, CA 94720, United States; Materials Sciences Division, Lawrence Berkeley Natl. Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, United States; Life Sciences Division, LBNL, 1 Cyclotron Road, Berkeley, CA 94720, United States; Materials Sciences Division, Physical Biosciences Division, LBNL, 1 Cyclotron Road, Berkeley, CA 94720, United States Department of Chemistry, Indiana University, Bloomington, IN 47405, United States

Affiliations Molec. Nanosci. and Catalysis Group, LCOO, Université Bordeaux I, 33405 Talence Cedex, France

USA

Japan

USA

USA

USA and Germany

USA

USA

Japan

France

Country France

Scientometric Assessment 17-9

17-10

Public Policy, Education, and Global Trends

TABLE 17.14 Most Productive Journals in the Field of Nanobiotechnology (1998–2017) Source Title Biosensors and Bioelectronics Proceedings of SPIE – The International Society for Optical Engineering RSC Advances ACS Applied Materials and Interfaces Biomaterials ACS Nano Langmuir Nanoscale Analytical Chemistry Journal of Nanoscience and Nanotechnology

References

TP 4,190

TC 15,1922

ACPP 36.26

h-Index 134

3,718

6,101

1.64

21

3,662 2,988

31,772 58,194

8.68 19.48

51 80

2,982 2,690 2,599 2,590 2,520 2,416

244,701 166,938 105,909 68,490 138,067 35,387

82.06 62.06 40.75 26.44 54.79 14.65

215 180 133 103 167 74

growth rate and Dt. Just one specific measure is not shrewd to power the assessment of researchers or of research groups. It is even unsafe, because it strengthens the opinion of administrators and politicians that scientific performance can be expressed simply by one note. Hence we always stress that a reliable set of several indicators is necessary, in order to explicate different aspects of performance. Hence to evaluate the author collaboration, CI and DC were employed to prove that 96% of the research outputs are of collaborative in nature. Similar to other disciplines collaborative research is predominance. In different approaches bibliometricians have dealt with this emerging area so far and presented a variety of data. This raises the question to what extent the data presented can provide useful information to various stakeholders, such as scientists and engineers, working in one of the fields related to nanobiotechnology, decision-makers in R&D administrations and industry. Some of the bibliometric data may be of interest to researchers in the field. Often they are specialists focused on one of the several strands in nanobiotechnology and may be interested in the data that provides them with a more general picture. They, just like policy analysts, may also be interested in the overall standing of countries in terms of publication output. Students of science and technology may find the parallel observation of publication data of interest. This study presented a summary of bibliometric research in the nanobiotechnology area. Using the publication of literature, an overview of bibliometric efforts have been presented to trace the emergence of this new technological area. The paper has also presented data that gives an idea about which countries are the most active in terms of scientific publications in nanobiotechnology. It is finally concluded that the results of a scientometric study that focuses on the field of nanobiotechnology are in increasing trend. However, it is sound to say based on the number of publications that the field of nanobiotechnology is currently led by the United States, China, and India. Countries that lack a R&D infrastructure to develop the nanobiotechnology may use scientometric trends to analyze the intrinsic variety in approaches to technology development.

Ajiferuke, I., Burell, O., and Tague, J. 1988. Collaborative coefficient: A single measure of the collaboration in research. Scientometrics 14: 421–433. Alonso, S., Cabrerizo, F., Herrera-Viedma, E., et al. 2010. hg-index: A new index to characterize the scientific output of researchers based on the h- and g-indices. Scientometrics 82: 391–400. doi: 10.1007/s11192-0090047-5. Bhattacharya, S. and Shilpa. 2013. China moving ahead in the global nanotechnology race: Evidences from scientometric study. COLLNET Journal of Scientometrics and Information Management 6(1): 97–117. doi: 10.1080/09737766.2012.10700927. de Solla Price, D. 1981. The analysis of scientometric matrices for policy implications. Scientometrics 3: 47– 54. Egghe, L. 2006. Theory and practice of the g-index. Scientometrics 69(1): 131–152. Garg, K.C. and Padhi, P. 2002. Scientometrics of laser research in India during 1970-1994. Scientometrics 55(2): 215–241. Hajar, S. and Nahid, K. 2014. Gender differences in science: The case of scientific productivity of nano science and technology during 2005–2007. Scientometrics 98: 457–472. doi: 10.1007/ s11192-0131031-7. Heinze, T. 2004. Nanoscience and nanotechnology in Europe: Analysis of publications and patent applications including comparisons with the United States. Nanotechnology Law and Business 1(4): 10. Hirsch, J.E. 2005. An index to quantify an individual’s scientific research output. Proceedings of National Academic Science USA 102(46): 16569–16572. Huang, Z., Chen, H., Chen Z.-K., et al. 2004. International nanotechnology development in 2003: Country, institution, and technology field analysis based on USPTO patent database. Journal of Nanoparticle Research 6: 325–354. Huang, Z., Chen, H., Li, X., et al. 2006. Connecting NSF funding to patent innovation in nanotechnology (2001– 2004). Journal of Nanoparticle Research 8: 859–879. Huang, Z., Chen, H., Yan, L., et al. 2005. Longitudinal nanotechnology development (1991–2002): National Science Foundation funding and its impact on patents. Journal of Nanoparticle Research 7: 343–376. Karki, M.M.S. and Garg, K.C. 1997. Bibliometrics of alkaloid chemistry research in India. Journal of Chemical Information and Computer Science 37: 157–161. Karpagam, R. 2014. Global research output of nanobiotechnology research: A scientometrics study. Current Science 106(11): 1490–1499. Karpagam, R., Gopalakrishnan, S., Natarajan, M., et al. 2011. Mapping of nanoscience and nanotechnology research in India: A scientometric analysis, 1990–2009.

Scientometric Assessment Scientometrics 89: 501–522. doi: 10.1007/s11192-0110477-8. Lawani, S.M. 1980. Quality, collaboration and citations in cancer research: A bibliometric study. Ph.D. dissertation, Florida State University, 4 February 2016. Lin, M.-W. and Zhang, J. 2007. Language trends in nanoscience and technology: The case of Chinese language publications. Scientometrics 70(3): 555–564. Liu, X., Zhang, P., and Li, X. 2009. Trends for nanotechnology development in China, Russia and India. Journal of Nanoparticle Research 11: 1845–1866. Mahapatra, M. 1985. On the validity of the theory of exponential growth of scientific literature. In 15 th ISASLIC Conference Proceedings, Bangalore, IASLIC. 61–70. Nagpaul, P.S. 1985 Contribution of Indian Universities to the mainstream scientific literature a bibliometric assessment. Scientomerics 32: 11–36. Prathap, G. 2010. The 100 most prolific economists using the p-index. Scientometrics 84: 167–172. doi: 10.1007/s11192-009-0068-0. Prathap, G. 2011. The fractional and harmonic p-indices for multiple authorship. Scientometrics 86(2): 239–244.

17-11 Prathap, G. and Gupta, B.M. 2009. Ranking of Indian engineering and technological institutes for their research performance during 1999-2008. Current Science 97(3): 304–306. Radrigo, C. and Maria, B. 2008. Is g-index better than h-index? An exploratory study at the individual level. Scientometrics 77(2): 267–288. Schellekens, M.H.M. 2010. Patenting nanotechnology: Are we on the right track? In: M.E.A. Goodwin, B.J. Koops, and R.E. Leenes (Eds). Dimensions of Technology Regulation (pp. 107–124). Nijmegen: Wolf Legal Publishers (WLP). Schubert, A. and Braun, T. 1986. Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics 14(2): 142–153. Shapira, P., Youtie, J., Porter, A.L. 2010. The emergence of social science research on nanotechnology. Scientometrics 85(2): 595–611. doi: 10.1007/s11192-010-0204-x. Subramanyam, K. 1983. Bibliometric studies of research collaboration: A review. Journal of Information Sciences 6(1): 33–38.

18 Progress in the Development of a Systematic Nanoperiodic Framework for Unifying Nanoscience 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-1 Assumptions and Background for a Proposed Systematic Framework for Unifying Nanoscience • Historical Background: Hierarchical Matter Is a Continuum of Quantized Building Blocks (QBBs) Defined by Six Critical Hierarchical Design Parameters (CHDPs)

18.2 Progress Leading to a Unified Nanoperiodic Framework for Nanoscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-21 Update of Original Nanoperiodic Hard/Soft Superatom Roadmap • Recent Progress with Soft Superatom Categories and Their Nano-Compounds • Engineering CNDPs of Soft Superatoms (i.e., Dendrimers) to Produce New Emerging Properties

18.3 Recent Update: Roadmap of Hard/Soft Superatom Categories, Combinatorial Libraries of Nano-Compounds and Nanoperiodic Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-31

Donald A. Tomalia National Dendrimer & Nanotechnology Center, NanoSynthons, LLC University of Pennsylvania Virginia Commonwealth University

Shiv N. Khanna Virginia Commonwealth University

18.1

Mendeleev-Like, Soft Superatom Periodic Table for Proteins • Proposed Involvement of Single Quantum State, Superatom Entities in Recently Reported Non-Traditional Intrinsic Luminescence (NTIL) Phenomenon • Confinement Leading to Superatom Quantum States Proposed as Critical Mechanism for Highly Luminescent Silver Nanoclusters in Sodalite Zeolites

18.4 Summary/Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-33 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-34 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18-34

Introduction

Perhaps the most influential force to have guided the advancement of traditional 19th-century atomic/molecular chemistry/physics theory to its current 21st-century maturity was the evolution of a “central paradigm/dogma.” Based on a shared consensus by both chemists and physicists, a similar central dogma for guiding/unifying nanoscience is emerging (Tomalia, 2009, 2010; Tomalia and Khanna, 2014, 2016). This new paradigm is based on the same first principles as traditional atomic/molecular theory and involves discrete nanoclusters of atoms [i.e., quantized building blocks (QBBs)] reminiscent of picoscale atoms. By analogy to traditional inorganic or organic elemental categories, they are referred to as hard or soft superatoms, respectively. These hard/soft superatoms have been observed and reported by both chemists and physicists. They are quantized, nanoscale atom clusters that behave as discrete atom-like units by mimicking unique nanoscale shell-filling (i.e., aufbau) features, as well as exhibiting discrete chemical combining properties leading to stoichiometric

nano-compounds/supramolecular assemblies (Tomalia, 2009, 2010; Tomalia and Khanna, 2014, 2016). Perhaps most compelling is that each of these hard and soft superatom categories exhibits unique nanoperiodic property patterns, much like traditional inorganic and organic type atomic elements. Intrinsic properties manifested by these hard/soft superatoms, as well as new emerging properties associated with chemical/supramolecular combinations of these nanoscale superatoms exhibit these periodic patterns. Much like the first list of 20 atomic elements and proposed stoichiometric combinations (i.e., compound atoms) published by Dalton in 1808 (Pullman, 1998) (Figure 18.1a), a preliminary roadmap of nanoscale elemental analog categories has been described. It currently consists of six major hard and six soft nano-element (i.e., hard/soft superatom) categories; however, these categories are expected to be expanded in the future. Furthermore, well-defined combinatorial libraries of potential nano-element compound/assemblies, nanoperiodic property patterns, nanoscale rules and proposed Mendeleev-like nanoperiodic tables are emerging as described in Figure 18.1b. 18-1

18-2

Public Policy, Education, and Global Trends

FIGURE 18.1 (a) Dalton’s first table of simple atoms, combining rules and proposed stoichiometries to produce compound atoms. (Adapted from Heilbronner and Dunitz (1993). Copyright 1993 Wiley-VCH Verlag GmbH & Co. KGaA.) (b) First proposed table of nanoscale superatom categories associated combining rules and nanoperiodic properties portending the possibility of Mendeleev-like nanoperiodic tables. (Adapted from Tomalia and Khanna (2016). Copyright 2016 American Chemical Society.)

These new observations, phenomena, principles and the concept first emerged independently within the chemistry and physics disciplines nearly a decade ago (Tomalia, 2009). Shortly thereafter, a consensus and convergence of thinking occurred between these two communities as a result of an invited lecture presented before the American Physics

Society (Boston, USA) in 2012 (Tomalia and Khanna, 2014). Within the context of this broad nanoperiodic concept, the physics world has focused largely on various hard superatom (i.e., inorganic like) examples (Tomalia, 2009; Tomalia and Khanna, 2016) and the role they might play in expanding our traditional periodic table to 3-D possibilities.

Nanoperiodic Framework for Unifying Nanoscience On the other hand, the chemistry community has focused more on the synthesis, characterization and role of soft superatoms (i.e., organic like) in this concept. The chemistry perspective was inspired by many amazing examples of atom mimicry exhibited by soft superatom categories such as dendrimers, nucleic acids and proteins. Extensive and detailed overviews of these activities are described elsewhere (Luo and Castleman Jr., 2014) and especially in a major review published recently (Tomalia and Khanna, 2016). This account begins with a brief discussion of assumptions and historical background for a proposed systematic nanoperiodic concept for unifying nanoscience. This discussion overviews major progress in the development of a unifying framework for nanoscience prior to 2015 followed by selected major advances from 2015 to the present. Recent progress includes the expansion and definition of hard/soft nano-element categories, new combinatorial examples of hard/soft nano-element compounds/assemblies, new routine synthesis of hard-superatoms (i.e., (Al)− 13 ), new emerging properties observed for atomically precise, hard superatoms (i.e., (Pt)n , n < 10) and recent examples of Mendeleev-like nanoperiodic tables all of which further validate this broad and unifying perspective.

18.1.1

Assumptions and Background for a Proposed Systematic Framework for Unifying Nanoscience

Intrinsic periodic property patterns exhibited by the chemical elements at the picoscale level (i.e., Mendeleev’s periodic table) have generally provided a wide range of discrete relationships for understanding the behavior of hierarchical matter. The periodic nature of these elemental patterns has allowed the routine prediction of many important physico-chemical properties as well as stoichiometric combining relationships exhibited by these fundamental building blocks. Over the past two centuries, these familiar picoscale patterns and relationships have led to first principles and a unified central dogma now adopted by both chemistry and physics to describe hierarchical matter. Until the 21st century, this scientific paradigm has focused largely on hierarchical matter at the sub-picoscale, picoscale (i.e., elemental) and sub-nanoscale (i.e., molecular) levels. This central dogma has provided critical insights into many important areas such as material sciences, engineering, agriculture, geology, biology, environmental sciences and medicine, to mention a few. These paradigm-driven activities have led to innumerable contributions from both chemistry and physics, which have enhanced the human condition. Unarguably, the emergence of nanoscience has been based upon the same traditional first principles and central dogma that have guided chemistry and physics since the 19th century. However, this very active nanoscale field has now produced a wide range of discrete new inorganic and organic structures/objects which are 103 larger and unlike any

18-3 familiar atomic/molecular entities at the pico- or subnanoscale level. This field has produced an abundance of interesting new phenomena and discrete nanoscale structures/objects derived from both hard and soft matter. Until recently, nanoscience has been viewed largely as an empirical activity in the absence of an equivalent Mendeleev-like taxonomy and unifying framework. As such, the emerging field of nanoscience over the past two decades has faced a critical challenge, namely, the need for a unifying central paradigm. Assumptions for Defining a Systematic Framework for Unifying Nanoscience Over the past decade, some progress has taken place and a unified central paradigm for nanoscience has emerged based on the following assumptions: • A “hierarchy of matter” has evolved from the subatomic to the macroscale level since the origin of the universe (i.e., ∼14 billion years ago). This hierarchy is a systematic size/complexity continuum formed by self-assembly of certain QBBs. • This size/complexity continuum is systematically populated with QBBs which are defined by six critical hierarchical design parameters (CHDPs), namely, (i) size, (ii) shape, (iii) surface chemistry, (iv) rigidity/flexibility, (v) architecture and (vi) elemental composition. • These CHDPs provide critical information at each dimensional level which is sequentially transferred in well-defined, assembly processes from QBB to QBB along a size/complexity continuum. • Discrete relationships existing between the QBBs throughout the size/complexity assembly continuum are manifested as “magic numbers” and usually indicate special stoichiometries or energy minima. • Either self-assembly or chemical combinations of QBBs lead to “symmetry breaking events” and enhanced complexity. As a result, these products exhibit new emerging properties which are regularly observed along the size/complexity continuum. • Elemental atoms are examples of QBBs at the picoscale level, whereas nano-element categories (i.e., hard/soft superatoms) are QBB examples at the nanoscale level. Preliminary tables of nanoelement categories have been proposed much as Dalton first proposed a list of 20 atomic elements in 1808. • QBBs at the nanoscale level often mimic QBBs at the atomic level as a function of their intrinsic properties (i.e., valence/geometric and combining properties) and are referred to as “hard or soft superatoms” (i.e., reminiscent of inorganic or organic elements).

18-4

Public Policy, Education, and Global Trends

• QBBs at the nanoscale level exhibit discrete and predictable nanoperiodic property patterns reminiscent of elemental atomic QBBs. • Nanoperiodic tables are emerging for both soft (i.e., dendrimers, nucleic acids and proteins) and hard superatoms (i.e., aluminum clusters and noble metal clusters).

18.1.2

Historical Background: Hierarchical Matter Is a Continuum of Quantized Building Blocks (QBBs) Defined by Six Critical Hierarchical Design Parameters (CHDPs)

A prevailing model believed to best describes the cosmological origin and evolution of hierarchical matter in the universe is referred to as the “Big Bang Theory.” This theory proposes that the universe originated approximately 14 billion years ago (Pullman, 1998; Rouvray and King, 2004) from a unique singular state. From this singular state, a time-dependent evolution first led to abiotic matter (i.e., molecules, macromolecules and assemblies) all of which were derived from traditional first chemistry/physical principles. With time, these initial abiotic substances self-assembled into a wide range of more complex higher dimensional matter. That withstanding, it is now recognized that much of this more complex matter is very well defined and may indeed be structurally quantized as a function of only six CHDPs, namely, size, shape, surface chemistry, rigidity/flexibility, architecture and elemental composition. It is believed that these CHDPs constitute important intrinsic structural information that may be sequentially transferred in nonchaotic assembly events to produce more complex, ordered matter. More specifically, it is proposed that this CHDP information is transferred via certain key hierarchical building blocks. When this CHDP information is transferred from the atomic/molecular to the nanoscale level, these parameters appear to direct and influence important assembly events, thereby producing well-defined stoichiometries, magic numbers and periodic property patterns beyond simple atoms and molecules. As these features are transferred and emerged in a variety of large nanoscale

structures (>1 nm), they appear to mimic traditional atoms at the picoscale level (Tomalia, 2009). As such, it is presumed that CHDPs play a significant role in the discrete evolutionary assembly of matter, at least penultimate to the micron-sized structures/assemblies required for the beginning of life. Breaking Symmetry Leads to New Emerging Properties Along the Hierarchical Size/Complexity Continuum According to Anderson (1972), when critical information (i.e., CHDPs) are transferred sequentially from subatomic particles [i.e., critical sub-atomic design parameters (CSADPs)] to higher complexity levels [i.e., CADPs → CMDPs (critical molecular design parameters) → CNDPs (critical nanoscale design parameters) → CMicDPs (critical micron-scale design parameters) → CMacDPs (critical macroscale design parameters)], one should expect hierarchical symmetry breaking to occur. These symmetry breaking events lead to new emerging properties, wherein a product resulting from a combination of precursor building blocks is not only more than but can be completely different than the sum of its parts (Figure 18.2). This is the very essence of pursuing molecular and nanoscale synthesis in a quest for new properties as a strategy to new product development. Hierarchical Transfer of CHDP Information, QBB Relationships and Magic Numbers It is widely accepted that most well-defined, hierarchical matter and complexity, both hard (i.e., inorganic) and soft (i.e., organic), is constructed according to some order associated with discrete, sequential multiples (i.e., magic numbers or stoichiometries) of QBBs, such as atoms, small molecules, monomers and polymers, yet major questions remain. What is the genesis of this order? What defines the parameters, principles, rules and strategies that direct these hierarchical assemblies? Is there a unifying system or framework for understanding this transfer of information and order? Could such insights produce a paradigm for “a priori” prediction of behavior and properties of matter beyond simple atoms and molecules?

FIGURE 18.2 A continuum of critical hierarchical design parameters from the sub-atomic (CSADP) to the macroscale level (CMacDPs). Concurrent symmetry breaking throughout this continuum leads to new emerging properties. (Reprinted with permission from Tomalia and Khanna (2016). Copyright 2016 American Chemical Society.)

Nanoperiodic Framework for Unifying Nanoscience In systems derived from discrete, quantized components according to well-defined rules, one usually observes interesting geometric or arithmetic patterns. These patterns may involve symmetries, repetitive structures, with or without scaling or regular sequences of numbers describing periodic quantities of the components. These patterns are not only aesthetically pleasing but, more importantly, may be used as predictive tools for understanding the behavior of these ordered systems. Recent thinking by Boeyens and Levendis (2010) has proposed that self-assemblies leading to well-defined complex matter are intrinsically rooted in the quantized and periodic features found in nucleons at the sub-atomic level (Boeyens and Levendis, 2010) (i.e., protons, neutrons and electrons). They propose that characteristic magic numbers exhibited by these quantized particles are derived from nucleon relationships based on number theory, as well as certain periodic properties and hidden symmetries (Wolfram, 2002). It is further believed that these CSADPs may be subsequently transferred to atomic structure and become manifested as familiar periodic behavior represented in the Mendeleev Periodic Table of the Elements. Such a transfer of CSADP information to higher levels of complexity is as shown in Figure 18.3. Either the association or linear combinations of these unique electron orbitals as they are presented at the atomic/molecular level produces a new ensemble of discrete relationships. These new molecular-level relationships are directed by the transfer of elemental structural information referred to as critical atomic design parameters (CADPs). Evidence for these discrete atomic or molecular structural relationships is readily observed in the formation of either 3-D atomic or 3-D molecular crystal lattices as illustrated in Figure 18.3. Large multiples of atoms and their compounds in bulk crystals organize into long-range periodic patterns according

18-5 to the unique features of their unit cells. As such, these atom or compound relationships are classified according to 1 of the 14 well-known Bravais lattices. As one considers the relationships between atoms or molecular/compound relationships present in size confined hard/soft nanoclusters (superatoms), one cannot always predict their intraand inter-cluster (i.e., aggregation) relationships using traditional paradigms. Primary structural repeating component (i.e., elemental unit cells) that determine the various Bravais latticetypes ultimately determine the extended periodic 3-D crystal lattice structure patterns. These extended 3-D Bravais lattices manifest unique periodic relationships between their constituent elemental atoms (i.e., usually metallic/inorganic) based on their CADPs as they interact with each other and grow to higher hierarchical dimensions. It is widely recognized that transfer of this CADP information occurs with high fidelity. As such, these information-transfer sequences involve atomic (CADPs) → molecular/sub-nanoscale (CMDPs) → nanoscale (CNDPs) → micron-scale (CMicDPs) → macroscale (CMacDPs) as the crystal lattices grow to macroscale dimensions clearly manifesting features of the unit cell-type from which they are derived. Essentially, all the metallic elements in the periodic table exhibit well-defined CADP-driven relationships as evidenced by their crystallization into one of several major crystal lattice-types as illustrated in Figure 18.4. It is noteworthy that according to mass spectrometry analyses, certain alkaline metal salts (i.e., NaI) are found to exhibit discrete, magic number, quantized multi-unit cell entities (i.e., building blocks). These closed-shell entities self-assemble into higher dimension lattices and ultimately bulk crystal lattices. This occurs with the complete hierarchical transfer of

FIGURE 18.3 Transfer of CSADP information to CADPs by combining nucleons with electrons and the transfer of CADP information to CMDPs by combining atomic orbitals to form molecular orbitals. (Adapted from Tweed (2003). Copyright 2003 Walker Publishing.)

18-6

Public Policy, Education, and Global Trends

FIGURE 18.4 Major crystal structures and unit cells for certain metallic elements (i.e., bulk state) in the periodic table resulting from transfer of CADP information to CMDP dimensions. (Adapted from Tweed (2003). Copyright 2003 Walker Publishing.)

CADP/CMDP/CNDP/CMicDP/CMacDP information as shown in Figure 18.5. This demonstrates unequivocal evidence for conservation of these CHDPs throughout the self-assembly process from the atomic to the macroscale state (Alonso, 2005). Our classification mirrors the classification of crystalline substances into various groups using the rotational groups. More recent validation of these QB information transfer events has been reported by Fernandez-Megia et al. (Amaral et al., 2018). They described a systematic hierarchical transfer of structural information as a function of dendrimer generation sizes which directly influenced the formation of polyion complex (PIC) architectures and their dimensions as described in Figure 18.6. By systematically decreasing dendrimer generations (i.e., sizes), larger PIC sizes were produced (i.e., 60–500 nm) accompanied by a micelle-tovesicle architecture transition which is interpreted according to a cone- to rod-shaped progression in PIC architecture. Reports of magic numbers and periodic property parameters associated with traditional atoms (Scerri, 2007) and molecular structures (Knight et al., 1984; Harbola, 1992) have been documented extensively throughout most of the 20th century (Boon and Sloane, 1985; Hargittai and

Hargittai, 1986). Beginning with primordial atom formation, their assembly required magic numbers of quantized subatomic particles to produce structure-controlled picoscale atoms (Pullman, 1998; Scerri, 2007, 2011), followed by combining magic numbers of atoms to produce stoichiometric molecular structures. As such, one might also expect similar “bottom-up, aufbau-like patterns/relationships” as a reasonable paradigm to account for the many discrete, quantized nanostructures that are derived from atomic matter. That withstanding, these discrete, monodispersed nanostructures/objects are indeed observed and constitute important categories of nanomaterials that in many cases exhibit atom mimicry or superatom properties. As such, they are distinguished from other less discrete, polydispersed nanoconstructs by expressing well-defined, quantized inter-relationship patterns, valencies and stoichiometries with each other (Tomalia, 2005, 2009). The quantized stoichiometries and inter-particle relationships exhibited by these well-defined nano-entities are (i) a consequence of their structure-controlled precursor building blocks and (ii) their ability to transfer important critical atomic design parameter (CADP) information to the molecular level, thereby defining CMDP information. This

Nanoperiodic Framework for Unifying Nanoscience

18-7

FIGURE 18.5 (a) Magic numbers obtained by mass analyses of discrete closed shell–quantized (NaI)n Na+ building blocks. (Reprinted with permission from Martin (1996). Copyright 1996 Elsevier Ltd.) (b) Growth patterns of (TiN)n ; (i) TOF mass spectrum of (TiN)n clusters. (ii) Proposed structures of (TiN)n clusters based on majic numbers observed in the mass spectrum. (Reprinted with permission from Chen and Castleman Jr. (1993). Copyright 1993 AIP Publishing.)

FIGURE 18.6 Size relationship of dendrimers to polyion complexes (PIC) involving the hierarchical transfer of CNDP information. (Reprinted with permission from Amaral et al. (2018). Copyright 2018 John Wiley & Sons.)

CMDP information is then articulately transferred to the nanoscale level, producing new information referred to as CNDPs. Thus, at each hierarchical level, it may be assumed that structure control of the six previously mentioned CHDPs must occur. This structural information transfer and control from the atomic to higher complexity levels is routinely observed in biological systems. The importance

of these hierarchical information transfer stages is clearly apparent in the construction of a macroscale, biological object such as a tendon (Figure 18.7). These transfer stages involve the sequential self-assembly of discrete building blocks beginning at the atomic level. They are defined by hierarchical size, shape, surface chemistry, flexibility, elemental composition, architecture and stoichiometric

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FIGURE 18.7 Hierarchical transfer of structural information from atoms to molecular structures and subsequently to nano- and micron-scale structures via quantized, structure-controlled hierarchical building blocks possessing “magic numbers” designated by a star. (Image adapted with permission from an original illustration courtesy of Prof. Eric Baer, Case Western Reserve University.)

combining relationships (i.e., magic numbers) as illustrated in Figure 18.7. Evidence for Periodic Property Patterns Based on CADPs in Mendeleev’s Periodic Table The quantized, structure-controlled properties observed for the atomic elements are widely recognized to produce

discrete periodic property patterns at the picoscale level. The structure-controlled properties leading to these periodic trends have been referred to as critical atomic design properties (CADPs) (Tomalia, 2009, 2010; Tomalia et al., 1990). Within the context of Mendeleev’s periodic table, these vertical and horizontal periodic trends based on CADPs are as illustrated in Figure 18.8 (Scerri, 2007, 2011).

FIGURE 18.8 Critical atomic design parameters (CADPs): structure-controlled (a) sizes, (b) shapes, (c) surface chemistry, (d) flexibility/polarizability, (e) architecture and elemental composition (Tomalia, 2012a). (Reprinted with permission of The Royal Society of Chemistry.)

Nanoperiodic Framework for Unifying Nanoscience In the case of abiotic systems, unique construction strategies assuring complete structural control are generally required for the formation of discrete, well-defined nanostructures and objects. The importance of understanding these bottom-up assembly protocols and aufbau patterns cannot be overstated. These approaches are invaluable for developing classification/taxonomy schemes for defining key soft and hard nano-building blocks (i.e., nano-element categories), as well as understanding their combinatorial assembly into constructs of higher complexity in the nanoscale region. In many respects, our present challenges are similar to historical 19th-century events in traditional chemistry that ultimately led to Mendeleev’s periodic table in 1869. Mendeleev’s seminal atomic element taxonomy has provided powerful insights and critical information useful for a prior prediction of elemental physico-chemical behavior, chemical reactivity, stoichiometries, assembly patterns, etc. leading to traditional small molecule structures/assemblies, as

18-9 well as their new emerging properties. In essence, the patterns and categorization of atomic elements in Mendeleev’s periodic table constituted an elegant classification or taxonomy for all the known atomic elements (Mendelejeff, 1869, 1889) that clearly organize and structure all the atomic elements according to certain critical atom design parameters (CADPs) as illustrated in Figure 18.8. Similarly, CHDP-controlled QBBs may be found throughout the abiotic hierarchical stair steps leading to higher structural complexity. These QBBs are generally characterized by several fundamental features, namely, (i) highly monodispersed (i.e., >90%) collections of atoms, (ii) they occupy well-defined space (i.e., 0, 1, 2 or 3D space) based on Pauli exclusion properties of their constituent atoms, (iii) they exhibit well-defined chemical or supramolecular stoichiometric relationships associated with (iv) discrete shapes or architectures as illustrated in Figure 18.9.

FIGURE 18.9 Hierarchical quantized building blocks (QBBs) as a function of structural complexity (i.e., dimensions, nm). These QBBs are structure controlled as a function of size, shape, surface chemistry, rigidity/flexibility, architecture and composition at each level of complexity (i.e., atomic, CADP; molecular, CMDP or nanoscale, CNDP), respectively. (Adapted from Kannan et al. (2014). Copyright 2014 John Wiley & Sons.)

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Atom Mimicry/Superatoms: Discrete Nanoscale Atom Clusters Functioning Collectively as Discrete, Atom-Like Building Blocks

The Parallel Worlds of Nanoscale Atom Mimicry: Physicists (Hard Superatoms/Nanoclusters) and Chemists (Soft Superatoms/Nanoclusters)

Mimicry of traditional electron aufbau, shell-filling characteristics recognized for atoms are pervasive and routinely observed for both hard and soft superatoms at the nanoscale level. More specifically, this mimicry involves metal atom, aufbau events leading to metal nanoclusters (i.e., hard superatoms) or monomer aufbau, shell-filling behavior to produce dendrimers (i.e., soft superatoms) as described in Figure 18.10. By analogy to traditional atoms, these superatom aufbau events may lead to highly reactive partially filled shells or relatively unreactive saturated shell levels mimicking noble gas configurations. For example, monomer aufbau, in the case of dendrimers, leading to saturated monomer shells (i.e., noble gas like configurations) produces non-autoreactive dendrimeric species (i.e., Figure 18.11a), whereas partial shellfilled dendrimers (i.e., Figure 18.11b) are found to be highly reactive species yielding dimeric or oligomeric products.

Hard Superatoms: A Historical Perspective Atomic clusters consisting of groups of three to a few hundred atoms provide another fertile area for the development and application of nanoperiodic concepts. It is in this size regime that quantum effects play a significant role enabling the clusters to exhibit a non-monotonic regime marked by properties often undergoing large changes with size and composition, to eventually converging toward the bulk behaviors at larger sizes. Early interest in atomic clusters began nearly 150 years ago when Faraday synthesized and demonstrated the size-dependent colors of colloidal gold in the 1850s (Faraday, 1857). This work provided initial insights into the unique colors associated with the famous Lycurgus cup and glass-coloring processes. The current interest in clusters is fueled by the developments in synthetic chemical techniques that allow formation of cluster materials with predesigned clusters as the building blocks. The properties of these assemblies can be controlled by altering

FIGURE 18.10 A heuristic comparison of electron, metal atom and monomer aufbau properties leading to traditional atoms, hard superatoms i.e., metal nanoclusters) and soft superatoms (i.e., dendrimers), respectively. (Adapted from Tomalia (2009). Copyright 2009 Springer Science + Business Media B.V.)

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FIGURE 18.11 (a) Heuristic comparison of saturated monomer shell dendrimers to saturated electron shells (i.e., noble gas configurations) observed in traditional atoms. (b) Heuristic illustration of a partial monomer shell-filled dendrimer mimicking the reactivity of a traditional chlorine atom. (Reprinted with permission from Tomalia et al. (2012). Copyright 2012 Cambridge University Press.)

the size and composition of the clusters, by ligating them, and/or by assembling them in different architectures. Extensive research over the last four decades has shown that the fundamental electronic properties including ionization energy, electron affinity, chemical valence, reactivity, magnetic properties and optical properties of clusters can change strikingly by adding, removing or replacing even a single atom or by adding ligands. The characterization and cataloging all of the potential properties of different clusters is a daunting task, and the organization of the enormous numbers and varieties of clusters into a useful framework can be an important step toward design of cluster assemblies with selected properties. A simple and elegant framework in which clusters may be organized is by characterizing those whose properties and reactivity may be approximated by those of an atom and behave like a “superatom.” In the following, we outline the development of this conceptual framework. Seminal work in the field of clusters began in the 1980s with the development of experimental techniques such as molecular beams, laser vaporization supersonic cluster beams, various spectroscopic techniques and chemical synthesis methods. These developments allowed the generation of mass selected clusters of virtually any element, wherein one could examine their properties one atom and one electron at a time. One could then address the question as to how new emerging properties appeared in the transition from (i) the discrete quantum conditions observed in small clusters, (ii) to boundary constrained properties as in nanoparticles (NPs) and (iii) to the bulk phase, wherein properties are insensitive to boundaries. The forces that bind atoms are electrostatic and one of the fundamental questions was if the observed properties show any periodic patterns seen in atoms. The first insight into this behavior came from the experimental work of Knight et al. (1984) where they generated size-selected Na clusters in molecular beams. It was astonishing that clusters containing 2, 8, 18,

20, 40, etc. atoms were more prominent than other sizes. These sizes were called “magic numbers” and were similar to magic numbers for atomic nuclei. More interesting were the observations of ionization energy and polarizability that showed a periodic pattern with magic clusters exhibiting higher ionization energies and lower polarizability. The origin of these periodic behaviors was rationalized with a simple jellium model, wherein the positive charge of the ionic cores are spread over the size of a sphere determined by the cluster size. The electronic states in such potential are grouped into shells where the angular and radial parts of the wave functions may be described using the spherical harmonics and a quantum number n. Clusters with filled shells and a large HOMO-LUMO gap exhibit enhanced stability and reduced polarizability. Later studies also revealed that these clusters exhibited chemical behaviors associated with closed electronic shells. This came out from the experiments on aluminum cluster anions reacted with oxygen. While bulk Al is known to be highly reactive to oxygen, the residual mass spectra of aluminum cluster anions reacted with oxygen showed large peaks at Al− 13 , − Al− 23 , and Al37 . These clusters have 40, 70 and 112 electrons respectively corresponding to closed electronic shells demonstrating that as in atoms, the chemical behavior in clusters can be linked to fill the electronic shells. Figure 18.12 shows the ordering of the shells in the icosahedral Al− 13 cluster and further reveals that the orbitals are similar both to the atomic orbitals of Cl− and the expected ordering from the jellium model. The electronic levels in Al− 13 correspond to |1S2 |1P6 |1D10 2S2 |2P6 1F14 ||2D0 1G0 | shell structure (single lines indicate gaps in energy, and double lines indicate the boundary between filled and unfilled orbitals). The number of valence electrons E v is determined by Eq. 18.1, where the cluster has N atoms, and V a corresponds to the number of valence electrons for each atom (1 for alkali, 2 for alkaline earth, 3 for aluminum and 1 for noble metals). Z is the net charge of the cluster.

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FIGURE 18.12 1995).

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Electronic levels in a Cl atom and a Cl− ion, compared with those in Al13 and Al− 13 clusters (Khanna and Jena,

Ev =

N 

Va − Z

(18.1)

1

For the electronic shell structure of a cluster to be well described by the spherical harmonics, the cluster must be approximately spherical and metallic so that it can be approximated by a confined nearly free electron gas. This analysis becomes more complicated when looking at transition metal clusters as the ordering of the nearly free electron gas is driven by the orthogonality principle, while atomic d orbitals are quite localized and contain two nodes that

complicate their incorporation into a nearly free electron gas. The power of this simple model is evident when we compare the atomic orbitals that form the backbone of the periodic table to the orbitals of metallic clusters as in Figure 18.13. This table does not plot the number of electrons but focuses on the frontier orbitals. In this way, alkali and alkaline earth atoms are part of the same column. Periodic patterns in the valence electron count of clusters lead to periodic closed electronic shell clusters. Experiments on larger clusters also indicated that clusters possessing complete geometric shells were more stable

FIGURE 18.13 Periodic patterns in the orbitals of atoms and metallic clusters. Each succeeding column corresponds to an orbital, not an additional electron.

Nanoperiodic Framework for Unifying Nanoscience (Martin et al., 1990). This electronic and geometric shell behavior led Khanna and Jena (1995) to propose that by combining electronic and geometric parameters, it should be possible to form stable clusters that could mimic chemical properties of elemental atoms in the Mendeleev periodic table and be regarded as superatoms, thus forming a third dimension of the periodic table as noted in Figure 18.14 (Bergeron et al., 2004a, 2005; Castleman Jr. and Khanna, 2009). Over the past 20 years, superatomic clusters mimicking the behavior of alkali, alkaline earth and magnetic elements have been identified. There is also evidence of clusters displaying a multivalence character as seen in some evidence. It has been possible to synthesize numerous assemblies with superatoms as building blocks. The conceptual superatomic framework developed initially for free clusters was later extended to ligated clusters. Studies by Whetten et al., Aikens et al. and others showed that the same considerations could account for the stability of metal clusters with ligands attached to the surface (Aikens, 2011; Hakkinen, 2012; Kumara et al., 2014; Walter et al., 2008). Their work on ligated Aun clusters showed that the ligands can act to withdraw the charge from the metallic core or form covalent bonds, thus localizing some of the charge and, consequently, changing the number of effective nearly free electrons in the metallic core. Through electronic structure studies, these authors showed that the stability of several synthesized cluster compounds + including Au104 (SR)44 , Au11 (PR3 ,X) and Au13 (PR3 )10 X23 with thiolate (PR) or phosphine and halide (PR3 , X) groups could be associated with the number of delocalized electrons attaining one of the magic numbers. For example,

FIGURE 18.14 Extension of 2-D Mendeleev’s periodic table into a 3-D perspective within the context of nanoscale, hard superatom, cluster-type elements.

18-13 the stability of the Au104 (SR)44 compound synthesized by Jadzinsky et al. (2007) could be understood within a superatom concept where the outer 44 Au atoms are linked to thiols, while the core of 58 remaining Au atoms contribute one electron corresponding to a closed electronic shell of 58 electrons (Ball, 2007). It is intriguing to note that the organizational patterns observed in atomic nuclei and atomic clusters may indeed extend to macroscale dimensions as observed by Shechtman for quasicrystals (Shechtman et al., 1984). The formation of such macroscale solids with quasi-periodic order is indeed striking. As such, atomic structure may be analyzed as a quasi-periodic arrangement of clusters. For example, in one class, the elementary building blocks are icosahedrons that combine to create an inflated self-similar icosahedron and so on as shown in Figure 18.15 (Janot, 1996; Khanna et al., 1995). The stability of these icosahedra relates to the magic numbers observed in elemental atom clusters, thus offering a “self-similar type” unified paradigm that appears to link nanoscale with macroscale quasi-periodic ordered behavior. Soft Superatoms: Early Perspectives Nurtured by Physicist, P.-G. de Gennes As early as 1983, Prof. Pierre de Gennes (College de France) and the coauthor (DAT) began a decade-long dialog concerning the extraordinary structural order and mathematically defined mass and surface group amplifications observed in soft matter such as dendrimers. As an attendee at the first public lecture on dendrimers [Winter Polymer Gordon Conference, January (1983), Santa Barbara, CA],

FIGURE 18.15 Planar section of the (AlPdMn) quasicrystal structure. Rings consisting of ten atoms are the primary building blocks. (Reprinted with permission from Janot (1996). Copyright 1996 American Physical Society.)

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FIGURE 18.16 (A) Mathematical expressions for calculating the theoretical number of surface groups (Z), branch cells (BC) and molecular weights (Mw) for [core : 1,2-diaminoethane]; (G = 0–7);{dendri-poly(amidoamine)-(NH2 )z} (PAMAM) dendrimers as a function of generation. Approximate hydrodynamic diameters (Gen = 0–7) based on gel-electrophoretic comparison for the corresponding PAMAM dendrimers. (Reprinted with permission from Tomalia (2010). Copyright 2010 The Royal Society of Chemistry.) (B) (a–f) Transmission electron micrographs (TEMs) of G = 5–10 PAMAM dendrimers. Sample (f) contains three molecules of G = 10 dendrimer for comparison. Bar length = 50 nm. (Reprinted with permission from Jackson et al. (1998). Copyright 1998 American Chemical Society.)

P. de Gennes became very excited about these quantized soft NPs. It was shown that these precise mathematically defined, monodispersed, spherical, soft matter structures could be routinely synthesized from commercial monomers in an iterative manner to produce precise, concentric, highly ordered branched monomer (i.e., branch cells) shells (i.e., generations) tethered around a central core (Figure 18.16a) (de Gennes and Hervet, 1983). This was the first example of a high molecular-weight synthetic polymer that could be precisely synthesized as a single mass structure, much like a small molecule or protein, without any molecular weight distribution. Evidence for the monodispersity of a typical dendrimer family (i.e., a Yamamoto-type dendrimer); [core:

p-phenylene]; (G = 4);{dendri-poly(phenylazomethine)} (DPA) dendrimers (Figure 18.17a) is demonstrated by MALDI-TOF mass spectroscopy as illustrated in Figure 18.17b. Using Soft Superatoms (i.e., Dendrimers) as Host Templates for the Synthesis of Hard Superatoms (i.e., Metal Nanoclusters) A pioneering work by Yamamoto and Imaoka (2014) has now shown that soft superatom dendrimers (i.e., phenylazomethane dendrimers) (Figure 18.18) may be used as template hosts for the precise atom by atom interior placement of guest metal atoms to form discrete metal nanoclusters. These metal encapsulation events occur

FIGURE 18.17 (a) Yamamoto-type dendrimer structure; [core: p-phenylene]; (G = 4); –dendri-poly(phenylazomethine)˝; (DPA) dendrimers. (b) MALDI-TOF of the G4; DPA dendrimer. (Reprinted with permission from Higuchi et al. (2001). Copyright: 2001 American Chemical Society.)

Nanoperiodic Framework for Unifying Nanoscience

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FIGURE 18.18 Schematic representation of layer-by-layer stepwise accumulation of metal chloride in phenylazomethine dendrimer (DPAG4). (Reprinted with permission from Yamamoto and Imaoka (2014). Copyright 2014 American Chemical Society.)

stoichiometrically as part of a very well-defined sequence beginning at the dendrimer core and proceeding outwardly as a function of dendrimer generation. These generationspecific ligation events are mathematically predictable to produce well-defined stoichiometry as illustrated in Figure 18.18. Subsequent metal reduction within these dendrimer templates produced a variety of precise “hard superatom

FIGURE 18.19 A dendrimer-based superatom synthesizer that allows programed atom by atom ligation of metals involving interior metal salt ligation followed by reduction to zero valent metal nanoclusters (metal superatoms). (Reprinted with permission from Yamamoto and Imaoka (2014). Copyright 2014 American Chemical Society.)

nanoclusters” which could be controlled “atom by atom.” Hence the “soft superatom dendrimer templates” have been referred to as hard superatom synthesizers as shown in Figure 18.19. As early as 1997, work by Tomalia et al. (Balogh and Tomalia, 1998; Balogh et al., 1999) demonstrated that commercially available poly(amidoamine) (PAMAM) dendrimers (NanoSynthons LLC, Mt. Pleasant, Michigan, USA) could also be used to encapsulate a wide variety of metal salts, including gold, silver, copper, cadmium, lead, nickel iron, cobalt, etc. which, when allowed to react with hydrogen sulfide, produced water-soluble metal sulfides in their encapsulated forms. Dendrimer-encapsulated metal salts, such as gold, silver and copper, were readily reduced to produce zero valent metal nanoclusters (Balogh and Tomalia, 1998; Balogh et al., 1999) as described in Figure 18.20. Subsequent work by Zheng et al. (2007) showed that these PAMAM dendrimers could be used as unique host templates for systematically assembling precise gold clusters (Au)n with metal multiplicities (n) = 3–38. They utilized tertiary amine moieties in the soft, superatomtype, PAMAM dendrimer interior as ligating templates much as described above by Yamamoto. By carefully

FIGURE 18.20 Schematic process involving templated metal complexation in the dendrimer interior, reduction of dendrimer interior complexed metal salts to zero valence metal clusters. This provides a precision “atom-by-atom” synthesis strategy for producing hard superatoms (i.e., metal nanoclusters). (Reprinted with permission from Balogh and Tomalia (1998). Copyright 1998 American Chemical Society.)

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FIGURE 18.21 (a) Measured and predicted ion mobility cross sections for gold cluster cations. (b) Calculated low-energy isomers of gold cluster cations. (Reprinted with permission from Gilb et al. (2002). Copyright 2002 AIP Publishing.)

controlling the amount of gold reagent added to the dendrimer followed by reduction, one could sequentially produce a systematic series of jellium-type, gold clusters; n = 3–12 as shown in Figure 18.21. Continued addition of gold readily led to the first saturated atom shell species, namely, n = 13. Quite surprisingly, these jellium-type gold superatoms exhibited an unprecedented new emerging property, namely, size-dependent fluorescent emission properties much as observed for traditional semiconducting, cadmium chalcogenide, quantum dots. Analogous to semiconducting quantum dots, small jellium-type gold nanoclusters (i.e., 800 nm) as shown in Figure 18.22a. Both excitation and emission maxima shift to lower energy emission as a function of gold concentration. This suggests that larger gold clusters are produced to give a size-tunable gold cluster fluorophore which may be engineered to emit as desired from the UV to the infrared region.

FIGURE 18.22 (a) Excitation (dashed) and emission (solid) spectra of different gold nanoclusters. Emission from the longest wavelength sample was limited by the detector response. Excitation and emission maxima shift to longer wavelength with increasing initial Au concentrations, suggesting that increasing nanocluster size leads to lower energy emission. (Reprinted with permission from Zheng et al. (2004). Copyright 2004 American Physical Society.) (b) Gold nanocluster (core) within the PAMAM dendrimer (shell) (i.e., AuNC@PAMAM).

Nanoperiodic Framework for Unifying Nanoscience TABLE 18.1 Gold Cluster Au5 Au8 Au13 Au23 Au31

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Photo Physical Properties of PAMAM-Encapsulated Gold Nanoclusters in Water Excitation (FWHM) eV 3.76(0.42) 3.22(0.54) 2.86(0.38) 1.85(0.21) 1.62(0.20)

Emission (FWHM) eV 3.22(0.45) 2.72 (0.55) 2.43 (0.41) 1.65 (0.26) 1.41 (0.10)

Quantum Yield % 70 42 25 15 10

Lifetime ns 3.5 7.5 5.2 3.6 –

Intrinsic Decay Rate (×109 GHz) 0.2 0.056 0.048 0.042 –

Source: Reprinted with permission from Zheng et al. (2004). Copyright 2004 American Physical Society.

Nanoscale Atom Mimicry: The Taxonomy of Hard/Soft Superatoms The jellium model is a critical guiding principle for evaluating new superatom candidates (Knight et al., 1984; Claridge et al., 2009). In this system, all the charges of the nuclei and core electrons in the cluster are uniformly distributed throughout the cluster spheroid. As such, the energy levels for electrons interacting with such a charge distribution correspond to 1S2 , 1P6 , 1D10 , 2S2 , 1F14 , 2P6 , etc. As opposed to traditional atomic energy levels, the first valence shell of a superatom contains s, p, d and f orbitals. Therefore, a filled superatom electron shell is defined by a magic number which coincides with an exceptionally stable filled electronic state (Knight et al., 1984; Claridge et al., 2009). Just as traditional atomic elements exhibit characteristic properties

that associate it with a particular group in the periodic table, so does this occur for the various superatom categories. Currently, superatom reactivities have been shown to mimic rare gases (Leuchtner et al., 1989), halogen atoms, alkaline earth elements (Bergeron et al., 2005), alkali metals (Reber et al., 2007), multivalent elements (Reveles et al., 2006) and magnetic atoms (Reveles et al., 2009). From this perspective, a general classification of various hard superatoms has evolved based on valence electron count and their mimicry of certain traditional element types. As such, at least four major categories are emerging for hard superatoms, namely, (i) superatom noble type gases (i.e., with a closed shell), (ii) super halogens (i.e., one electron less than a closed shell), (iii) super alkalis (i.e., one electron more than a closed shell) and (iv) superatomic alkali earth metals as illustrated in Figure 18.23a. Similarly, this same heuristic

FIGURE 18.23 (a) A taxonomy of hard superatom categories and their electron filling properties relative to traditional elements and a heuristic comparison of hard superatom categories to traditional atomic element types based on outer shell electron count. (b) A taxonomy of soft superatom categories (i.e., dendrimers) and their monomer filling properties relative to traditional elements in the periodic table (see Figure 18.11a,b for a comparison with chlorine). A heuristic comparison of soft superatom categories to traditional atomic element types based on outer shell monomer count (see Figure 18.10). (Reprinted with permission from Tomalia and Khanna (2016). Copyright 2016 American Chemical Society.)

18-18 comparison to traditional element types can be made with soft superatoms such as dendrimers. By invoking a monomer count in the dendrimer outer shell or generation (see earlier discussion and Figure 18.10), emergence of these same four superatom categories as described above become apparent and are as indicated in Figure 18.23b. Recent dialog between chemists and physicists, beginning in 2012 (Tomalia, 2012b) (Invited lecture to the American Physics Society) (Tomalia and Khanna, 2014), has led to the realization that each discipline have been working in parallel worlds for over two decades concerning the general concepts of nanoscale atom mimicry, nanoscale superatoms, nano-elements and nano-periodicity (Kemsley, 2013). This consensus has recognized that more complex, large nanoscale collections (i.e., 103 larger than atoms) of discretely organized atoms may manifest many physicochemical and building block features reminiscent of individual atoms (Hirsch et al., 2000; Bergeron et al., 2004b). These chemically bonded or supramolecularly assembled collections of atoms are generally structure-controlled entities that exhibit well-defined sizes (i.e., masses), shapes, surface chemistries (i.e., valency), flexibilities/rigidities, atomic compositions and architecture. They are now referred to as nanoscale superatoms (Hirsch et al., 2000; Bergeron et al., 2005, 2004b; Reveles et al., 2006), atom equivalents (Zhang et al., 2013), nano-elements (Tomalia, 2009), heuristic atom mimics (Tabakovic et al., 1997; Tomalia, 1997, 2005, 2009, 2010) or artificial atoms (Tomalia and Khanna, 2014). Physicists have focused primarily on nanoscale atom mimicry associated with inorganic, hard superatom electron orbital behavior and electron shell closure. Defined as any collection of atoms that exhibits certain characteristic properties of elemental atoms, an early example of a hard superatom was the observed clustering of sodium atoms from the vapor state into magic numbers of atom clusters (i.e., 2, 8, 20, 40 and 58). The first two magic numbers (i.e., 2 and 8) are recognized as the number of electrons required to fill the first and second shells, respectively. Thus, superatom mimicry is related to new orbitals defined by free electrons associated with the atom collection in the cluster rather than each individual atom. Chemically speaking, superatoms appear to behave analogous to traditional atoms in a way that allows them to attain closed shells of electrons in this new cluster orbital counting scheme. Many examples of hard superatoms involving metal atom clusters have been reported by pioneering physicists including Khanna, Castleman, Jena etc. (Bergeron et al., 2004b, 2005), Hakkinen (2008) and others (Ball, 2005). On the other hand, chemists have focused on nanoscale atom mimicry (Tomalia, 2009, 2010; Munoz-Mamol et al., 2015; Tomalia and Frechet, 2005) associated with welldefined nano-valency, nano-sterics, nano-stoichiometries, monomer aufbau and shell closings. These special features and properties have been associated largely with soft, organic hetero-atomic nanoclusters such as dendrimers

Public Policy, Education, and Global Trends and were first noted in the 1990s (Tomalia, 1993, 1994). For example, dendrimers possessing unfilled outer monomer shells were observed to be highly autoreactive, leading to dimer or oligomer formation. In contrast, ideal outer shell–saturated dendrimers behaved like noble gas atomic elements and did not exhibit this auto-reactivity. Other discrete soft NPs, such as proteins, viral capsids, DNA/RNA, nano-latexes, polymeric micelles and monodispersed synthetic polymers, have also exhibited certain nanoscale atom mimicry features of equal interest. Many of these hetero-atomic, soft, organic nanoclusters exhibit chemical and supramolecular combining patterns that produce well-defined stoichiometries and closed shell–type behavior normally associated with naked, elemental atoms. These hard/soft superatoms or nanoscale atom mimics appear to fulfill a pivotal role as nanoscale building blocks much as elemental atoms function at the pico/sub-nanoscale level. As such, these poly(atomic) structures/entities have been classified and referred to as hard and soft nanoelement categories (Tomalia, 2009, 2010). Furthermore, these nano-element categories have been shown to form stoichiometric nano-compounds/assemblies that exhibit welldefined intrinsic nanoperiodic property patterns much as atomic elements and their compounds. In fact, this nanoscale atom mimicry constituted a primary hypothesis upon which a new nano-periodic system for unifying nanoscience was proposed (Tomalia, 2009). More specifically, it provided a fundamental paradigm for explaining why many well-defined nanoscale building blocks (i.e., both soft/hard nano-elements) were observed to combine in well-defined stoichiometries and exhibit nanoperiodic property patterns reminiscent of traditional atomic elements. In the context of this perspective and using traditional chemistry first principles initiated by Lavoisier, Dalton, Mendeleev and others, a new systematic framework for unifying and defining nanoscience was proposed. Just as 19th-century first principles led to a central paradigm and a periodic system for traditional elemental atom/small molecule chemistry, it was proposed that a similar nanoperiodic system might be defined for discrete, well-defined nanomodules as described in the next section. A Roadmap of Hard/Soft Superatom Categories, Combinatorial Libraries of Nano-Compounds and Nanoperiodic Patterns These abovementioned issues were examined as a critical theme for a National Science Foundation (NSF) Workshop (2007) entitled: Periodic Patterns, Relationships and Categories of Well-Defined Nanoscale Building Blocks (Tomalia, 2008). An early consensus of principles, classifications and fundamentals was initiated at this workshop and subsequently evolved into a systematic framework for unifying and defining nanoscience. This conceptual framework was based on first principles derived from traditional chemistry/physics and included (i) a

Nanoperiodic Framework for Unifying Nanoscience nanomaterials classification roadmap, (ii) a table of welldefined nano-module (element) categories (i.e., superatoms), (iii) combinatorial libraries of nano-compounds/assemblies and (iv) many observed examples of nanoperiodic property patterns in the literature. This concept was initially published as an NSF report in 2008 (Tomalia, 2008) and subsequently as a peer-reviewed journal article in 2009 (Tomalia, 2009) with recent enhancements (Tomalia, 2009, 2010; Tomalia et al., 2012; Tomalia and Khanna, 2014). It focused exclusively on well-defined, monodisperse (0-D/1-D) nanoscale materials and the division of these well-defined materials into hard and soft NP categories. These categories followed features associated with traditional inorganic and organic materials, such as rigidity/flexibility, architectural criteria and elemental compositions (Figure 18.24). There is now substantial evidence showing that CADP and CMDP information may be strictly conserved and efficiently transferred to these hard/soft nano-element categories/superatoms by using appropriate synthesis and assembly protocols (Tomalia, 2009; Tomalia et al., 1990; Yamamoto and Imaoka, 2014; Luo and Castleman Jr., 2014). These construction protocols are based on specific assembly principles that allow strict structural control of their CNDPs. By controlling these CNDP features, it was found that these structurecontrolled nano-constructs exhibited certain traditional features/behavior of atoms (i.e., atom mimicry) and are now referred to as hard/soft nano-element categories or hard/soft superatoms (Kemsley, 2013).

18-19 As such, a primary feature is their ability to react or selfassemble to form combinatorial libraries of stoichiometric, Hard-Hard, Hard-Soft or Soft-Soft nano-compounds or nano-assemblies (Figure 18.24). Furthermore, it was found that these hard/soft nano-elements/superatoms and their nano-compounds/assemblies exhibited many characteristic nanoperiodic property patterns usually associated with atoms. These nano-property trends/periodic property patterns may be classified as either intrinsic physicochemical or functional/application-type properties and are pervasive and abundant throughout the literature (Tomalia, 2009, 2010, 2012a; Tomalia et al., 2012). In all cases, these unique nanoperiodic patterns appear to be inextricably related to their structure-controlled CNDPs. Furthermore, these CNDP directed patterns/trends are beginning to define nanoperiodic rules and Mendeleev-like periodic tables much as observed for traditional elemental atoms and their CADPs in the 19th century and illustrated earlier in Figure 18.8. First Examples of Mendeleev-Like Nanoperiodic Tables Mendeleev-Like Nanoperiodic Tables for Predicting the Self-Assembly of Amphiphilic Dendrons As early as 1992, Percec et al. (1992) compared the selfassembly of abiotic, amphiphilic dendrons (i.e., [S − 1]; soft superatoms) with the self-assembly of biological protein subunits (i.e., [S − 4]; soft superatoms) to produce cylindrical viral capsids. Percec’s work (Percec,

FIGURE 18.24 Concept overview: Using first principles and step logic that led to the “central dogma” for traditional chemistry, the criteria of nanoscale atom mimicry was applied to well-defined Hard and Soft nanoparticles. This produced 12 nano-element types which were classified into six Hard particle and six Soft particle nano-element categories. Chemically bonding or assembling these Hard and Soft nano-elements leads to Hard:Hard, Soft:Hard or Soft:Soft type nano-compound categories, many of which have been reported in the literature. Based on the discrete, quantized features associated with the proposed nano-elements and their compounds, an abundance of nano-periodic property patterns related to their intrinsic physico-chemical and functional/application properties have been observed and reported in the literature. (Reprinted with permission from Tomalia (2009). Copyright 2009 Springer Science + Business Media B.V.)

18-20 2006) was inspired by Klug’s seminal Nobel investigation (Klug, 1983, 1999) that defined the stoichiometric structure of cylindrical Tobacco Mosaic Viruses (TMV). Percec was able to show unequivocally those dendrons and protein sub-units behave as “quasi-equivalent,” quantized, soft superatoms to produce stoichiometric cylindrical or spherical supramolecular dendrimers much as observed in the formation of all biological viral capsids. Especially convincing were the discrete and predictable stoichiometries (n) that were observed by Percec for the self-assembly of these [S − 1]-type soft superatoms (i.e., amphiphilic dendrons) which produced the various stoichiometric, spherical supramolecular dendrimers [S − 1n ] described in Figure 18.25. A complete historical overview of Percec’s work leading to this present perspective may be found in Balagurusamy et al. (1997) and Percec et al. (1998, 2000a,b, 2001, 2004, 2006, 2007). It is noteworthy that key CNDPs, such as (i) size, (ii) shape, (iii) surface/apex chemistry and (iv) flexibility, directly determined the stoichiometric values (n) for the self-assembly of these soft superatoms [S − 1] into their respective supramolecular dendrimers [S − 1]n (Rosen et al., 2009a). These (n) values ranged from single to double digit numbers as described in Figure 18.26.

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FIGURE 18.25 Self-assembly of Percec-type amphiphilic dendrons (i.e., [S − 1] type nano-elements) into spherical supramolecular dendrimers (i.e., [S − 1]n , where : n = discrete, stoichiometric aggregation numbers that range between 72 and 155 for various [S − 1]n type stoichiometric nano-compounds/nanoassemblies). (Adapted from Percec et al. (2008). Copyright: 2008 American Chemical Society.)

The First Predictive, Mendeleev-Like Nanoperiodic Tables Subsequent work by Rosen et al. (2009a) clearly showed that retro-structural analyses of tertiary and quaternary supramolecular assemblies obtained in Figure 18.27

FIGURE 18.26 Structural and retro-structural analysis of supramolecular dendrimers [S − 1]μ derived from the self-assembly library of AB2 ; 3,4-disubstituted-(PBp) type amphiphilic dendrons; [S−1]. (Adapted with permission from Rosen et al. (2009a,b). Copyright 2009 American Chemical Society.)

Nanoperiodic Framework for Unifying Nanoscience

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FIGURE 18.27 Dependency of self-assembly patterns leading to tertiary and quaternary dendron assemblies based on primary structure-controlled dendron CNDPs such as size, shape, surface chemistry and flexibility. (Adapted with permission from Rosen et al. (2009b). Copyright 2009 American Chemical Society.)

produced CNDP directed, nanoperiodic patterns based on primary structures for these [S − 1]-type dendron structures that could be utilized to predict x-ray confirmed tertiary and quaternary structures as described in Figure 18.27 with an accuracy of 85%–93%. Application of these nanoperiodic patterns/rules to extensive combinatorial libraries of these [S − 1]-type soft superatoms produced some of the first examples of predictive, Mendeleev-like nanoperiodic tables by simply knowing only four CNDPs for the primary [S − 1]-type amphiphilic dendrons, namely, (i) size, (ii) shape, (iii) apex/surface chemistry and (iv) flexibility (Tomalia, 2009, 2010; Tomalia et al., 2012). These first Mendeleev-like periodic tables and nanoperiodic principles demonstrating the importance of using CNDPs associated with amphiphilic dendrons and dendrimers for predicting self-assembly patterns with greater than 82% accuracy have been reviewed extensively elsewhere (Sun et al., 2015).

18.2

Progress Leading to a Unified Nanoperiodic Framework for Nanoscience

18.2.1

Update of Original Nanoperiodic Hard/Soft Superatom Roadmap

Recent Progress with Hard Superatom Categories and their Nano-compounds The original nanoperiodic hard/soft superatom roadmap published earlier (Tomalia and Khanna, 2016) is illustrated in slightly expanded form as shown in Figure 18.29. Just as Dalton’s original table of the traditional atomistic elements published in 1808 changed substantially with time, so it is expected that the current categories and

features of hard/soft superatom-based nano-elements are expected to expand dramatically in the future. As illustrated in Figure 18.28, expanded scope and definitions for certain hard/soft categories have already appeared recently in the literature and will be described in this section. Most notable, has been the evolution of hard superatom categories (i.e., [H − 5] fullerenes and [H − 6] carbon nanotubes) into an expanded category of carbon nano-allotropes which now includes fullerenes, nanotubes, carbon dots, graphene, nanodiamonds and various combined carbon superstructures nanostructures. A scheme describing the dimensionality and possible inter conversion between these various carbon nano-allotropes is as described in Figure 18.29. These dramatic advances have been reviewed extensively elsewhere (Georgakilas et al., 2015). Substantial progress has been made in the development of hard superatom cluster design rules (i.e., magic numbers, geometries, electron counting rules, magnetic properties, etc.), as well as the use of hard superatoms as fundamental building blocks in energy storage and catalyst applications. These important issues have been reviewed extensively elsewhere by Jena and Sun (2018). More specifically, controlled synthesis protocols allowing “precise atom count” for hard superatom clusters (i.e., metal/metal salt clusters) have now been reported for a variety of hard superatom categories. Earlier investigations of hard superatoms have generally only involved theoretical predictions supported by mass detection in the gas phase under high vacuum conditions. However, recent work by Yamamoto et al. (Kambe et al., 2017) has reported the unprecedented solution phase synthesis of a widely recognized aluminum nanocluster, namely, Al− 13 , a hard superatom. Using a specifically designed poly(phenylazomethine) (DPA) dendrimer (i.e., a soft superatom) as a host template, it was demonstrated that an exact number of aluminum

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Public Policy, Education, and Global Trends

FIGURE 18.28 Modified nanoperiodic roadmap defining general criteria for nano-element categories which include well-defined (i.e., monodispersed) hard/soft superatom-type nanoparticles, atom mimicry, ability to form stoichiometric nano-compounds and exhibit nanoperiodic property patterns based on CNDPs, namely, size, shape, surface chemistry, flexibility/rigidity, architecture and elemental composition. (Reprinted with permission from Tomalia (2009). Copyright 2009 Springer Science + Business Media B.V.)

atoms could be encapsulated and size controlled to produce these precise metal clusters in solution as illustrated in Figure 18.30a–c. A similar templating protocol was found to stabilize these materials for use as superatom building block intermediates by generating the superatoms in solutions with capping ligands or other protecting shells thus preventing sintering of the clusters. The resulting clusters could then be deposited on substrates and were found to be stable against exposure to air/oxygen. This stability against oxidation is reminiscent of the original gas phase inertness of Al− 13 that led to the initial identification of Al− as a superatomic cluster. 13 Although stabilizing ligands may be used to protect these clusters against undesirable reactivity, ligand stabilization

may also be used to alter the reducing characteristics of atom clusters. This unusual feature was demonstrated recently by Chauhan et al. (2018) who showed how certain organic ligands may lower the ionization energy of superatom clusters, irrespective of their shell closure. This effect was demonstrated by successive attachment of three N -ethyl-2-pyrrolidone (EP = C6 H11 NO) ligands to a bare Al− 13 and doped MAl12 (M=B, C, Si and P) clusters containing 39, 40, and 41 valence electrons. For PAl12 , the addition of ligands lowers the ionization energy to 3.25 eV which is lower than any element in the periodic table as illustrated in Figure 18.31. A unique feature associated with controlling size or a “precise atom count” in small metal clusters is that the

Nanoperiodic Framework for Unifying Nanoscience

FIGURE 18.29 Scheme showing interconversion between various nanoallotropes highlighting the change in dimensionality. One-sided arrows represent one-way transformations, whereas twosided arrows denote possible two-way transformations (Georgakilas et al., 2015).

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FIGURE 18.31 Ligated Al− 13 based superatoms exhibiting very low ionization energies.

− FIGURE 18.30 (a) Assembly of Al− 13 cluster using a dendrimer template, (b) mass spectrum of Al13 in the dendrimer and the − − dendrimer without Al13 , (c) a SEM of the cluster in the dendrimer and (d) Al13 cluster diameter in nanometers (Kambe et al., 2017) http://creativecommons.org/licenses/by/4.0/.

18-24 catalytic properties are known to vary markedly based on those two parameters. Studies by Imaoka et al. (2013) demonstrated that the catalytic activity of Ptn clusters for oxygen reduction reactions increased almost two fold as cluster size was decreased from 13 to 12 atoms as illustrated in Figure 18.32. It is recognized that Pt13 has a compact icosahedral structure, whereas the removal of a single Pt atom leading to a Pt12 cluster produces a missing atom in the first coordination shell. The resulting deformed structure consequently yields several highly active, lower coordination sites that exhibit dramatically enhanced catalytic activity. From this theoretical perspective, Khanna et al. (George et al., 2015) pioneered the experimental work that demonstrated the importance of using ligand-assisted synthesis to produce atom precise platinum thiol crowns stabilized by glutathione (GSH). This appears to be the first successful attempt to theoretically and synthetically control the atomicity of platinum nanoclusters without the use of a dendrimer template.

Public Policy, Education, and Global Trends These glutathione protected clusters are well known for their solubility in water and their stability compared to other thiolate clusters (George et al., 2015). More recently, Yamamoto et al. (Imaoka et al., 2017) successfully synthesized an unprecedented series of monodispersed tiara-like Pt nanoclusters with atomicity n = 5–12 as illustrated in Figure 18.33. These tiara-like nanoclusters (i.e., Pt5 –Pt12 ) were characterized by STEM as shown in Figure 18.34. A seminal article by Reber and Khanna (2017) provides many critical examples demonstrating that both electronic and geometric shell-filling events are necessary concepts for understanding the reactivity of metal clusters. These important shell-filling phenomena provide fundamental organizational principles and a periodic concept by which to understand well-defined valencies and specific interactions associated with metal cluster–based hard superatoms (Figure 18.13). As such, the superatom concept provides a unifying paradigm for understanding the reactivity of

FIGURE 18.32 Size selective synthesis of discrete platinum clusters, wherein poly(imino) dendrimers were used as templating ligands for synthesizing Pt12 and Pt13 , respectively. (Reprinted with permission from Imaoka et al. (2013). Copyright 2013 American Chemical Society.)

FIGURE 18.33 Tiara-like Pt complexes: (a) chemical structures of Pt complexes, (b) chromatogram of preparative HPLC separation with size exclusion columns, (c) MALDI-TOF mass spectra of Pt complexes, (d) optimized geometric structures of Pt complexes with atomicity values; n = 5–13 (Imaoka et al., 2017) http://creativecommons.org/licenses/by/4.0/.

Nanoperiodic Framework for Unifying Nanoscience

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FIGURE 18.34 Synthesis of monodispersed P12 clusters from tiara-like complexes: (a) direct reduction of tiara-like complexes to Pt12 in a hydrogen atmosphere, (b) XPS of complexes before and after reduction step, (c) a dark field STEM image of the reduced complexes and (d) atomic-scale high-magnification STEM images of the monodispersed clusters (Pt5 –Pt13 ) with scale bar = 1 nm (Imaoka et al., 2017) http://creativecommons.org/licenses/by/4.0/.

FIGURE 18.35 Combining superatom building blocks (A and B) to produce a combinatorial library of superatomic crystals (SAC) (i.e., hard/hard superatomic nano-compounds; see Figure 18.28) which have exhibited many new collective properties including tunable electrical transport, thermal conductivity and ferromagnetism (Pinkard et al., 2018).

metal clusters ranging from free aluminum clusters to ligand protected noble metal clusters, as well as metal-chalcogenide ligand protected clusters. Perhaps most compelling is that superatom properties are generally controlled by enhanced stabilities when they retain their closed electronic and geometric shells which are indeed reminiscent of traditional atom theory. More recently, this superatom building block concept was applied to a broad class of hard superatom metal clusterfullerene-based materials to yield many new emerging properties suitable for doped semiconductors with tunable chemical potential/optical band gaps or materials with tunable electrical/thermal transport properties (Choi et al., 2016; Khanna and Reber, 2017; Lee et al., 2014; O’Brien et al., 2017; Roy et al., 2013; Turkiewicz et al., 2014). These superatom building blocks synthesis involved a central core of transition metals and chalcogens ligated with various ligands to produce various hard superatom-derived nano-compounds exhibiting superatomic crystal features, magnetic ordering, as well as unique phonon/electronic transport properties as described in Figure 18.35 (Pinkard et al., 2018). These hard superatom-based building blocks are highly stable and may be independently prepared in solutions. They have charge donor/acceptor characteristics and can form either unary or binary solids with complementary units while maintaining the identity of the internal structure. The resulting new class of superatom-based nanocompounds (see Figures 18.35 and 18.36), some of which have open electronic shells, may exhibit non-magnetic or magnetic materials. These interesting nano-compounds confirmed the earlier atom mimicry concept proposed for

hard superatoms (Tomalia, 2009; Tomalia and Khanna, 2016). These investigators succeeded in synthesizing superatomistic crystalline solids by the assembly of hard superatoms acting as electron donors that exchanged charge with another hard superatom category (i.e., C60 counterions) to form the ionic solids (Roy et al., 2013). Roy et al. initially reported three such solids including [Co6 Se8 (PEt3 )6 ][C60 ]2 , [Cr6 Te8 (PEt3 )6 ][C60 ]2 and [Ni9 Te6 (PEt3 )8 ][C60 ]. In each case, the core containing chalcogenide-based clusters is decorated with 6 or 8 tri-ethylphosphine (PEt3 ) ligands that are connected to the metal atoms, thus protecting the metallic core. The ligated clusters donate charge and combine with C60 that acts as charge acceptor to form CdI2 or NaCl ionic solids (Figure 18.36). The ligated superatoms Co6 Se8 (PEt3 )6 have lower ionization energies for removing electrons beyond the first ionization. Such a feature allows them to be used as dopants for semiconductors. In a recent study, Reber and Khanna (2018) examined a WSe2 surface doped with ligated metal-chalcogenide Co6 Se8 (PEt3 )6 clusters. These superatoms are characterized by valence quantum states that can readily donate multiple electrons. It was found that the WSe2 support binds more strongly to the Co6 Se8 cluster than the PEt3 ligand, so ligand exchange between the phosphine ligand and the WSe2 support is energetically favorable. The metal-chalcogenide superatoms serve as a donor that may transform the WSe2 p-type film into an n-type semiconductor. The theoretical findings complement recent experiments where WSe2 films with supported Co6 Se8 (PEt3 )6 are indeed found to undergo a change in behavior from p- to n-type. It was further shown that by replacing the PEt3 ligands with CO ligands, one can control the electronic character of the surface and deposited species.

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FIGURE 18.36 Library of hard superatom-based nano-compounds (i.e., superatomic crystals (SAC)) self-assembled using charge transfer between two categories of hard superatomic building blocks. Supporting ligands have been omitted for clarity (Pinkard et al., 2018).

FIGURE 18.37 Structures of binary cage superatoms (BCSs); metal atom (i.e., Ti and Ta)–encapsulated Si16 cage superatoms and Si atom–encapsulated Al12 cage superatoms, respectively. (Reprinted with permission from Tsunoyama et al. (2018). Copyright 2018 American Chemical Society.)

Very recently, Nakajima et al. (Tsunoyama et al., 2018) reported the synthesis and characterization of discrete metal atoms incarcerated in Si16 cluster cages or a Si atom encapsulated in an Al12 cluster cage as described in Figures 18.37 and 18.38.

18.2.2

Recent Progress with Soft Superatom Categories and Their Nano-Compounds

Precise Polymer Synthesis Involving Controlled/Living Polymerization Protocols and Mechanisms As described in the early versions of soft superatom/nanoelement categories (Figures 18.24 and 18.28), the six initially proposed soft superatom categories were evenly divided between synthetic and biologically derived systems. This was largely due to a lack of available synthetic protocols to produce required monodisperse systems which are critical criteria for qualifying as a soft superatom/nano-element category. Perhaps, the only exception was the soft superatom category of dendrimers. However, the field of synthetic polymer chemistry has developed dramatically recently, wherein the synthesis of well-defined precision macromolecules with controlled compositions, chain ends, chain lengths, molecular weight distribution and topology are

becoming commonplace (Lutz et al., 2016; Mansfield et al., 2010). Although early advances leading to precision polymers began as early as the 1950s (Szwarc, 1956), development of other protocols, such as controlled radical polymerization (Matyjaszewski and Xia, 2001; Ouchi et al., 2009), dendrimer synthesis (Bosman et al., 1999; Fréchet and Tomalia, 2001; Tomalia et al., 2012; Tomalia and Fréchet, 2002), iterative solid-phase chemistry (Lutz et al., 2013; Merrifield, 1985) and supramolecular polymerizations (Aida et al., 2012; DeGreef et al., 2009), have dramatically broadened the range of precision polymers that are now possible. Based on the well-defined features and monodisperse properties exhibited by many of these precise polymer systems, it is now appropriate to reevaluate many of these systems as possible new candidates for inclusion in a more expanded version of soft superatom (i.e., soft nanoelement) categories. Currently, major approaches leading to well-defined precision systems consist of three broad approaches strategies and many sub-strategies as described in Figure 18.39. It is remarkable that only five decades after the discovery of the living cationic polymerization of oxazolines (Bassiri et al., 1967; Tomalia and Sheetz, 1966) some of the most complex supramolecular architectures discovered in soft condensed matter (i.e., liquid quasi-crystals) can now be obtained with these protocols as described in the following subsections. Living Cationic Polymerization Leading to Precise Linear-Poly(Oxazolines) → Periodic/Quasi-Periodic Self-Assemblies Liquid quasicrystals (LQC) are quasiperiodic arrays which lack long-range translational periodicity and are approximated by two Frank-Kasper periodic arrays, namely, Pm3n cubic (Frank-Kaspar A15) and P42 /mnm tetragonal (Frank-Kaspar alpha). Although LQCs have been observed in certain soft matter systems, to date, the self-assembly of poly(oxazolines) into LQCs has not

Nanoperiodic Framework for Unifying Nanoscience

18-27

FIGURE 18.38 Thermal stability of Ta@Si16 BCSs on C60 layers. STM images at (a) RT, (b) 433 K and (c) 508 K, (d) possible − conformers of Ta@Si− 16 − C60 on the surface, XPS of Ta@Si16 BCSs deposited on C60 /HOPG at different temperatures, (e) and (f). (Reprinted with permission from Nakaya et al. (2015), Ohta et al. (2016). Copyright 2016, 2015 American Chemical Society.)

FIGURE 18.39 Classification of major synthetic strategies for producing precise polymers. Three major approaches include (i) stepgrowth polymerization, (ii) chain-growth polymerization, (iii) multi-step growth synthesis. ADMET, acyclic diene metathesis; ARGET, activators generated by electron transfer; ATRP, atom transfer radical polymerization; CAR, initiators for continuous activator regeneration; NMP, nitroxide-mediated polymerization; RAFT, reversible addition-fragmentation chain-transfer polymerization; ROMP, ring opening metathesis polymerization; SARA, supplemental activator and reducing agent. (Reprinted with permission from Lutz et al. (2016). Copyright 2016 Macmillan Publishers.)

been observed. Recently, Percec et al. (Holerca et al., 2018) have reported the predictable self-assembly of minidendronized poly(oxazolines) (i.e., poly(3,4) 17G1-Oxz) into either periodic or quasiperiodic arrays based solely on their degree of polymerization as described in Figure 18.40. It

is both remarkable and quite surprising that such a rich array of discrete, predictable assemblies may be selectively formed within such a narrow range of poly(oxazoline) DPs (i.e., 5–10). This work demonstrates the amazing structural sensitivity that is involved in producing these discrete and

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Public Policy, Education, and Global Trends

FIGURE 18.40 Summary of periodic and quasiperiodic arrays self-organized from assemblies of poly[(3,4) 17G1-Oxz]. (Reprinted with permission from Holerca et al. (2018). Copyright 2018 American Chemical Society.)

predictable self-assemblies. Furthermore, this work provides further evidence for the value of analyzing CNDP-based nanoperiodic self-assembly patterns (i.e., DP = size) as a strategy for a priori predictions of soft superatom assembly modes and structures. Nanoscale Atom Mimicry of DNA Leading to Directional Bonding, Long Range Ordered Lattices, Hybridization of Hard/Soft Superatom Categories into Nano-compounds and DNA Origami Building Blocks Both linear DNA and RNA were originally recognized as a major soft, superatom nano-element category (i.e., Category S-6) in the proposed nano-elemental tables based on their critical roles as fundamental nanoscale building blocks and connectors (Tomalia, 2009). As early as 1996, Seeman et al. (Li et al., 1996) investigated DNA as a discrete category of soft superatom-type building blocks involving complementary DNA hybridizations. They were used for constructing a vast array of 2-D and 3-D nanoscale lattices/architectures generally referred to as DNA-origami (Seeman, 2010) (Figure 18.41, right panel).

Nearly simultaneously, Mirkin et al. (1996) reported the importance of using DNA as discrete nanoparticle template connectors for assembling a wide range of programable DNA-NP lattices that demonstrated atom equivalency or atom mimicry (Zhang et al., 2013) (Figure 18.41, left panel). This mimicry was demonstrated by assembling a variety of spacer controlled, metal-cluster lattices (Macfarlane et al., 2011), the formation of stoichiometric dendrimer– based nano-compounds (DeMattei et al., 2004), as well as other directional nanoconstructs (Jones et al., 2015) as illustrated in Figure 18.41. A hybridization strategy utilizing building blocks based on “caged NP guests within a DNA origami host” was reported to produce a diamond-like family of NP superlattices (Liu et al., 2016) exhibiting atom mimicry. Spherical Nucleic Acids: Significant DNA-Nanoparticle Conjugates (i.e., DNA Based Nano-Compounds) More recently, Mirkin et al. (Cutler et al., 2012) reported the construction of spherical DNAs derived from the grafting of thio-terminated DNAs to the surface of gold nanoclusters to

Nanoperiodic Framework for Unifying Nanoscience

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FIGURE 18.41 Historical development of nanoscale DNA bonds as directional, atom-like connectors leading to nanoparticletemplated DNA constructs and hybridization-based DNA constructs pioneered by C.A. Mirkin and N.C. Seeman, respectively. Reprinted with permission from (Jones et al., 2015). Copyright 2015 American Association for the Advancement of Science.

produce DNA–AuNP conjugates or other discrete NPs (i.e., proteins, silicas and metal clusters) possessing a range of DNA graft densities based on salt aging protocols as illustrated in Figure 18.42. Spherical DNAs have received extensive attention for a variety of nanomedicine applications including delivery of anti-cancer drugs (Bousmail et al., 2017) RNAi-based therapy for glioblastoma (Jensen et al., 2013), synthesis of spherical RNA (Rouge et al., 2014), clinical chemistry (Mirkin and Petrosko, 2018) and enhanced immunomodulatory activity (Radovic-Moreno et al., 2015).

18.2.3

Engineering CNDPs of Soft Superatoms (i.e., Dendrimers) to Produce New Emerging Properties

Hierarchical Transfer of Information from Dendrimers to Polyion Complexes Micellar and vesicular PICs are nanoassemblies prepared from oppositely charged polymers and PEGylated block

copolymers (Yoon et al., 2014). These nanoassemblies are attractive as drug delivery systems (DDS) because of their electronic neutrality, stealth character and narrow size distribution. Despite that, a serious shortcoming to their widespread use has been the inability to engineer/tune the size of these assemblies for specific DDS applications. Fernandez-Megia et al. (Amaral et al., 2018) have reported that dendrimers may be effectively used for the transfer of hierarchical information to PIC nanoassemblies to control their sizes. By simply engineering the CNDP size parameter of a dendrimer (i.e., generation), it is possible to control the size of a PIC and hence dramatically affect their biodistribution properties in DDS applications. More specifically, it was found that by decreasing the dendrimer size (i.e., generation), the PIC sizes were dramatically enhanced (i.e., from 80 to 500 nm) accompanied by micelle to vesicle transitions. This hierarchical transfer of information was interpreted according to a cone- to rod-shaped progression in the architecture of the minimum PIC assemblies (i.e., uPIC) involved in the PIC assembly growth as illustrated in Section 18.1.2.2,

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FIGURE 18.42 (a) Synthesis of spherical DNA (i.e., DNA–AuNP conjugates) involving thiol-nucleotide adsorption onto an AuNP core followed by salt aging to enhance the DNA shell density. (b) Construction of various spherical DNA conjugates by grafting linearDNA onto either hard (i.e., metal NPs and silica NP) or soft (i.e., cross-linked/coordination polymer, protein, liposome or micelle type) cores. (Reprinted with permission from Cutler et al. (2012). Copyright 2012 American Chemical Society.)

Figure 18.6. As such, larger dendrimers produced smaller PICs, an effect associated with their higher rigidity and multivalency, whereas smaller dendrimers produced larger PICs. This unique hierarchical transfer of information to PICs was architecturally dependent and was not observed for linear polymers. Dendrimer-Based Terahertz Radiation Generators Suitable for Commercial THz Spectrometers Pioneering work by Rahman (Rahman et al., 2016b), Dalton (Dalton et al., 2010) and others (Tomalia, 2012a) has shown that engineering the functionality and architecture of soft superatoms such as dendrimers has produced a wide range of enhanced non-linear optical (NLO) properties. This earlier work provided the groundwork and basis for recent work by Rahman et al. (2017) who have shown

that by engineering three CNDP’s (i.e., size, surface chemistry and interior composition) for PAMAM dendrimers it was possible to produce new emerging hyper-polarizable dendrimer properties suitable for high-quality terahertz generation and the production of commercial THz spectrometers (Rahman and Rahman, 2011). More specifically, engineering the dendrimer size (i.e., G = 5), the surface chemistry (i.e., modified with 3-acryloxypropyl trimethyloxysilane) and doping the dendrimer interior with an alizarin dye produced a CNDP-engineered hyper-polarizable dendrimer that generated high power CW THz radiation (i.e., 10 μm–1 mm) when exposed to a pulsed laser, as shown in Figure 18.43. These hyper-polarizable dendrimer generators were found to produce THz radiation based on a new, unprecedented radiation generation mechanism referred to as dendrimer dipole excitation (DDE) (Rahman et al., 2016a).

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18.3.2

FIGURE 18.43 Engineering three poly(amidoamine) (PAMAM) CNDPs, namely, size, surface chemistry and interior composition, led to hyper-polarizable dendrimer substrates that upon exposure to a pulsed laser produced highquality (CW) terahertz radiation according to a new “dendrimer dipole excitation” mechanism suitable for use in commercial THz spectrometers. (Reprinted with permission from Rahman et al. (2017). Copyright 2017 Royal Society of Chemistry.)

18.3

18.3.1

Recent Update: Roadmap of Hard/Soft Superatom Categories, Combinatorial Libraries of Nano-Compounds and Nanoperiodic Patterns Mendeleev-Like, Soft Superatom Periodic Table for Proteins

An endless assembly of simple proteins (i.e., soft superatoms) is required to produce more complex, well-defined quaternary structures which are critical to biological processes required for sustaining life. In general, these critical assemblies occur via three basic assembly types, namely, (a) cyclization, (b) dimerization and (c) subunit addition. Quite remarkably, systematic analyses of these basic protein assemblies have revealed that well-defined periodic assembly patterns based on CNDPs, such as size, shape, surface chemistry, flexibility/rigidity and architecture, underpin all of these critical assembly processes. As such, Ahnert et al. (2015) have proposed a “nanoscale, protein periodic table” which allows the systematical “a priori” prediction of quaternary protein complex structures based on simply knowing the primary protein structures, assembly types and stoichiometries as described in Figure 18.44a,b.

Proposed Involvement of Single Quantum State, Superatom Entities in Recently Reported Non-Traditional Intrinsic Luminescence (NTIL) Phenomenon

As early as 2000–2001, Goodson/Tomalia et al. (Varnavski et al., 2001) and Larson and Tucker (2001) reported inexplicable blue fluorescence properties for soft superatom type, PAMAM dendrimers, although these structures did not possess any traditional aromatic or conjugated poly(ene) moieties. These blue luminescence properties are generally characterized/associated with excitation maxima (E ex = 320–400 nm) and emission maxima (E em = 410– 520 nm) and were initially attributed to adventitious traditionally luminescent impurities. After nearly two decades, this characteristic blue fluorescent behavior has now been observed in a wide range of non-aromatic, non-polyene structures, reported in >150 peer-reviewed publications and extensively reviewed elsewhere by D.A. Tomalia et al. This behavior has been shown to be an intrinsic property unequivocally associated with the clustering/confinement of a wide range of non-aromatic, electron-rich moieties (i.e., amines, amides, imines, hydroxyl, carboxylic and pyrrolidones) coined heteroatomic sub-fluorophores (HASLs). This new phenomenon is referred to as non-traditional intrinsic luminescence (NTIL) and luminescence occurs when these non-emissive, electron-rich HASL assemblies are clustered/ confined by any of four criteria, namely, (I) architecturally, (II) chemical cross-linking, (III), supramolecularly or (IV) physically, as described in Figure 18.45. It is widely recognized that extreme reductions in temper◦ ature (i.e., approaching 0 K) lead to the formation of Bose–Einstein condensates and force integration of individual quantum states into a single, superatom-like quantum state as shown in Figure 18.46. Similarly, one might visualize that the four HASF confinement strategies leading to NTIL behavior may be mimicking the Bose–Einstein condensate trajectory by forcing individual HASF quantum states into a more integrated single quantum state with superatom features due to molecular rigidification/immobilization as illustrated in Figure 18.45.

18.3.3

Confinement Leading to Superatom Quantum States Proposed as Critical Mechanism for Highly Luminescent Silver Nanoclusters in Sodalite Zeolites

At least four confinement strategies (i.e., architectural, chemical cross-linking, supramolecular assembly and physical confinement) have been proposed as critical mechanisms leading to NTIL behavior as discussed in Section 18.2.6 (Figures 18.45 and 18.46). A related physical confinement mechanism has recently been proposed by Grandjean et al. (2018), wherein they have observed extraordinarily bright green luminescent properties for small physically confined

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FIGURE 18.44 (A) Three major assembly steps (i.e., (a) cyclization, (b), dimerization and (c) subunit addition) which are dependent on CNDPs such as size, shape, surface chemistry, flexibility/rigidity and architecture. (B) Periodic table of assembly patterns, (C) predicted topologies and quaternary structures of assembled proteins. (Reprinted with permission from Ahnert et al. (2015). Copyright 2015 American Association for the Advancement of Science.)

FIGURE 18.45 Non-emissive, electron rich, heteroatomic sub-fluorophores (HASFs) may be integrated into reactive monomers, reactive hydrophobes, miscellaneous substrates or soft superatoms (i.e., dendrimers) and transformed into NTIL active emissive clusters/assemblies by (I) architectural confinement, (II) chemical cross-linking, (III) supramolecular assembly or (IV) physical confinement. (Reprinted with permission from Tomalia et al. (2019) Copyright 2018 Elsevier.)

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FIGURE 18.46 A Bose–Einstein condensate trajectory forcing individual HASF quantum states to hybridize into a single superatomlike quantum state of highly delocalized electrons is presented. This low temperature Bose–Einstein-induced molecular structure confinement is analogous to proposed room temperature based: (a) architectural, (b) chemical crosslinking, (c) supramolecular, or (d) physical confinement of HASF moieties associated with the NTIL phenomenon. (Reprinted with permission from Tomalia et al. (2019) Copyright 2018 Elsevier.)

silver nanoclusters. These few atom-silver clusters consist of 2+ hydrated tetrahedral Ag4 (H2 O)x (x = 2–4) clusters and are incarcerated within the sodalite domains of Linde Type A (LTA) zeolites as illustrated in Figure 18.47. Their optical properties were shown to originate from a confined two electron superatom quantum system with hybridized Ag and water O orbitals delocalized over the cluster (Quintanilla and Liz-Marzan, 2018). Upon excitation, one electron of the s-type highest occupied molecular orbital is promoted to the p-type lowest unoccupied molecular orbital and relaxes through enhanced intersystem crossing into long-lived triplet states.

18.4

Summary/Conclusions

In this brief progress review, we presented early examples and rationale for unifying both soft and hard nanoscale elemental clusters/assemblies into a general concept based on widespread observations of nanoscale atom mimicry and nanoperiodic property patterns. Atom mimicry, reminiscent of simple traditional atoms, is now a well-recognized phenomenon observed for both discrete hard and soft nanoscale assemblies. It is from this foundation that the manipulation of nanoscale matter may be visualized in the simplified context of superatoms as discrete building blocks. It provides a unifying foundation upon which to systematically understand, design and engineer application directed construction of nanoscale matter by manipulating six CNDPs, namely, sizes, shape, surface chemistry, flexibility/rigidity, architecture and elemental compositions. Simple engineering of only six simple CNDP parameters has led to many unprecedented and dramatically new emerging

FIGURE 18.47 Structural representation of a hydrated Ag4 2+ cluster, i.e., tetrahedral Ag4 (H2 O)x (x = 2−4) confined within the sodalite cage domain of a zeolite. This confinement is believed to give rise to a superatom quantum system in which the silver and water oxygen orbitals become highly delocalized over the cluster to produce highly luminescent entities reminiscent of behavior observed for the non-traditional intrinsic luminescence (NTIL) phenomenon described in Section 18.2.6 (Figures 18.45 and 18.46). (Reprinted with permission from Quintanilla and Liz-Marzan (2018).

18-34 properties. The possibility of creating and assembling new classes of hard/soft superatom building blocks portends the development of many new materials for energy harvesting, storage and conversion, as well as in the medical/life sciences for imaging, drug design and drug delivery.

Acknowledgments We are grateful to Ms. Linda S. Nixon for providing important graphics, as well as for assembling and critical editing this review. DAT is grateful to the National Science Foundation for support through NSF Award #0707510 and would especially like to acknowledge important discussions with Dr. Mihail Roco (NSF), Dr. David Hedstrand (NanoSynthons LLC), Prof. N.J. Turro, Columbia University (deceased), Prof. P.G. de Gennes, College de France (deceased), as well as many others associated with the NSF Workshop (2007) entitled: Periodic Patterns, Relationships and Categories of WellDefined Nanoscale Building Blocks (Tomalia, 2008). SNK is grateful to Dr. Art Reber for useful discussions and the U.S. Department of Energy (DOE) through grant DE-SC0006420, and Air Force Office of Scientific Research under Grant number FA9550-18-1-0511 for financial support.

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Index A

C

Academic knowledge research, 1-8 Action learning innovation cycle, 13-5 Action Plan for Nanotechnology, 8-1, 8-4 Activity Index (AI), 17-6 AFM, see Atomic Force Microscopy (AFM) Africa, nanotechnology in, 5-9 AI, see Activity Index (AI) Allotropes of Carbon. Are There Any Buckyballs?, 13-10–13-11 ‘Amara’s Law’, 14-4 Amphiphilic dendrons, 18-19, 18-20–18-21 Arnold and Mabel Beckman Foundation, 5-5 Asante Africa Foundation, 5-3 ASTM International, 8-3 Atomic clusters, 18-10–18-13 Atomic Force Microscopy (AFM), 2-6, 9-6, 12-8, 12-9 Atomic-level crystal structure, 3D printing, 14-9–14-15 Atom mimicry/superatoms, nanoscale, 18-10, 18-25, 18-33 confinement strategies, 18-31, 18-32, 18-33 of DNA, 18-28, 18-29 hard superatoms, 18-10–18-13, 18-25 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 soft superatoms, 18-13, 18-14 as host templates, 18-14, 18-15–18-17 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 Aufbau patterns, 18-6, 18-9–18-11 Author collaboration, 17-4 Authorship pattern, 17-4

CAI, see Co-authorship Index (CAI) California NanoSystems Institute (CNSI), 5-2, 5-4 Cambridge Structural Database (CSD), 14-16 Carbon Nanotechnology Tubes (CNTs), 4-3, 4-5 “Catch 22,” 12-11–12-12 CERN laboratory, 3-1 Chain-linked model, 1-3 CHDPs, see Critical hierarchical design parameters (CHDPs) CI, see Collaboration Index (CI) CIFs, see Crystallographic information framework files (CIFs) Cif2VRML program, 14-6, 14-7, 14-16 Cingolani’s thesis, 8-2 Citation exploration, 17-1 classification, labeling, and packaging (CLP) theory, 2-11 ‘Classroom 3D printing,’ 14-1, 14-4, 14-5 for atomic-level crystal structure, 14-9–14-15 for biomedical applications, 14-9 for bioscientific applications, 14-9 crystallographic information framework files, 14-5–14-6 for crystal structure models, 14-15–14-16 ‘Jmol Crystal Symmetry Explorer,’ 14-15–14-16 Microsoft Windows™, 14-6–14-9 print-out times, 14-5 Closeness centrality, 16-3 CNDPs, see Critical nanoscale design parameters (CNDPs) CNSI, see California NanoSystems Institute (CNSI) CNTs, see Carbon Nanotechnology Tubes (CNTs) Co-authorship Index (CAI), 17-5, 17-6 COD, see Crystallography Open Database (COD) Cognitive affordances, 7-3 Collaboration Index (CI), 17-4 Cooperative Patent Classification (CPC), 1-8, 1-9 CPC, see Cooperative Patent Classification (CPC) Critical hierarchical design parameters (CHDPs), 18-3, 18-4; see also Mendeleev nanoperiodic tables atom mimicry/superatoms, 18-10 hierarchical transfer of, 18-4–18-8 nanoscale atom mimicry (see Nanoscale atom mimicry) periodic properties on, 18-8–18-9 Critical nanoscale design parameters (CNDPs), 18-7, 18-19–18-22 Crosscutting concept scale, proportion, and quantity, 11-2 structure and function, 11-2–11-3 systems, 11-2

B BCI, see Brain-Computer Interfaces (BCI) Beckman Scholars Program, 5-3 Betweenness centrality, 16-3 Bibliometric networks, 16-2, 16-5, 16-6–16-8 Bibliometric studies nanotech innovations, 1-6–1-7 on nanotech scientific production, 1-4–1-5 on scientific literature, 1-5–1-6 Big Bang theory, 18-4 Biomedical applications, 3D printing, 14-9 Biomimetic materials, 1-6 Biopolymer accession code, 14-9 Bioscientific applications, 3D printing, 14-9 Bloom’s taxonomy approach, 15-4–15-5 Brain-Computer Interfaces (BCI), 8-5, 8-6 Bravais lattices, 18-5 Brunauer-Emmett-Teller (BET) method, 2-7

I-1

I-2 Crystallographic information framework files (CIFs), 14-1, 14-2, 14-16 of dextrorotatory crystalline right-α-quartz, 14-10 inorganic crystal structure, 14-10–14-15 morphology data, 14-8 open-access sources for, 14-5–14-6 Crystallography Open Database (COD), 14-5, 14-10, 14-11, 14-15–14-17 Crystal morphology, 14-6–14-8 CSD, see Cambridge Structural Database (CSD) Cutting-edge science and nanotechnology, 13-6, 13-7

D Davidson Institute of Science, 5-3 DC, see Degree of Collaboration (DC) DDS, see Drug delivery systems (DDS) Degree centrality, 16-3 Degree of Collaboration (DC), 17-4 Degree of tissue complexity, 2-9 Delphi study, 6-3, 9-2, 9-4, 9-8 Dendrimers, 18-7 atom mimicry, 18-10 cluster using, 18-23, 18-24 monomer shell, 18-11, 18-18 poly(amidoamine), 18-14–18-17, 18-30, 18-31 to polyion complexes, 18-7, 18-29, 18-30 soft superatoms engineering CNDPs of, 18-7, 18-29, 18-30, 18-31 vs. hard superatoms, 18-14, 18-15–18-17, 18-26 supramolecular, 18-20 Terahertz radiation generators, 18-30–18-31 Yamamoto-type, 18-14 Design-based research, NSET, 12-11–12-13 Designing Effective Science Instruction, 10-5–10-6 Dispersing medium, 2-9 Dissemination and Training in Nanotechnology (NANODYF), 15-6–15-7 Dissemination, outreach, and training Bloom’s taxonomy approach, 15-4–15-5 importance of, 15-2 commercial and business reasons, 15-3 social motivations, 15-3–15-4 NANODYF network, 15-6–15-7 overview of, 15-1–15-2 scientific and technological, 15-2–15-6 DLS, see Dynamic Light Scattering (DLS) DNA-origami, 18-28 Doubling Time (Dt), 17-3, 17-4 Drug delivery systems (DDS), 18-29 Dynamic Light Scattering (DLS), 2-6

E ECHA, see European Chemicals Agency (ECHA) Economic competitiveness, 6-1 Educational programs, in nanotechnology, 5-2–5-5 EDX, see Energy-dispersive X-ray spectroscopy (EDX) EFSA, see European Food Safety Authority (EFSA)

Index EIT, see European Institute of Innovation and Technology (EIT) ELI, see Extreme Light Infrastructure (ELI) ELI Nuclear Physics (ELI-NP), 3-1, 3-6 Energy-dispersive X-ray spectroscopy (EDX), 2-6 Engineering nanoscience, 5-7 Environmental Protection Agency, 4-3 Environmental risks, 4-6 European Chemicals Agency (ECHA), 2-11 European Commission, 2-12, 3-9, 8-1, 12-1, 15-3 European Food Safety Authority (EFSA), 2-11, 2-12 European Institute of Innovation and Technology (EIT), 8-3–8-4 European regulations, on nanomaterials, 2-9, 2-11–2-12 European Union Action Plan, 8-4 Extreme Light Infrastructure project, 3-1, 3-4 framework programs, 16-2 Graphene Flagship project, 5-1 legislation on nanomaterials, 2-9, 2-11–2-12 nanoeducation in, 8-1, 8-3–8-4, 8-7 thematic program, 8-3 Experiential learning, 13-3–13-4, 13-5 Extreme Light Infrastructure (ELI), 3-1, 3-4 Ex vivo methods, 2-10

F Fabrication approaches, NST, 9-7–9-9 Feynman’s stature, 4-2 FFF, see Field-Flow Fractionation (FFF) Field-Flow Fractionation (FFF), 2-7 Firms innovation, 1-7–1-11 FLIM, see Fluorescence Lifetime Analytical Microsystems (FLIM) Fluorescence Lifetime Analytical Microsystems (FLIM), 2-7 FPs, see Framework programs (FPs) Framework programs (FPs), 16-2

G Gartner’s hype cycle, 14-3, 14-3–14-5 General-purpose technology (GPT), 1-2 Genetic structuralism, 13-4 Geometric/arithmetic patterns, 18-5 Gold nanoparticles, 4-3, 9-7, 11-6, 18-16 GPT, see General-purpose technology (GPT) Grades 7–12 instruction, 10-7, 12-2 Graduate education, 6-3 Graphene bibliographic analysis of, 16-4–16-5 conceptual structural map of, 16-6 co-word analysis of, 16-5, 16-6 Graphene Flagship project, 5-1

H HA, see Hydroxyapatite (HA) Hard superatoms, 18-1, 18-2, 18-10–18-13

I-3

Index nanoperiodic roadmap, 18-21–18-26 roadmap of, 18-18–18-19 synthesizers, 18-15 taxonomy of, 18-17–18-18 Heteroatomic sub-fluorophores (HASFs), 18-31–18-33 High school students next generation science standards crosscutting concept of, 11-1–11-3 performance expectation, 11-3 sense-making phenomena, 11-3 potential curriculum organizational schema nanoscale characteristics, 11-5–11-7 scale worlds, 11-3, 11-4, 11-5 Hydroxyapatite (HA), 1-7 Hype cycle graph, 14-3–14-5

I IBSE, see Inquiry-Based Science Education (IBSE) ICP, see International Collaboration of publications (ICP) ICP-OES, see Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) ICT, 13-6, 13-8 Incremental innovation, 1-3 Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), 2-6 Innovation bibliometric studies on, 1-4–1-7 definition of, 1-2 for entrepreneurship, 1-2–1-3 firms, 1-7–1-11 linear model of, 1-3 mechanisms of, 1-2 nanotechnologies and, 1-1–1-2 case studies of, 1-6–1-7 for entrepreneurship, 1-7–1-11 historical path of, 1-6 types of, 1-3 Inorganic nanomaterials, 2-4 Inquiry-Based Science Education (IBSE), 13-8 In silico methods, 2-10 Integrating nanoscience in high school continuous integration of, 10-5–10-8 as curriculum insertion, 10-6–10-7 modular approach to, 10-2–10-5 NanoLeap, 10-2–10-3, 10-5 NanoSense project, 10-3, 10-4 NanoTeach Project, 10-2, 10-5–10-7 overview of, 10-1–10-2 teachers’ views on, 10-7–10-8 Interactional expertise, 6-4 Interactive visualization technology, 7-1–7-3; see also Virtual nanoworld Interdisciplinarity research, 8-2–8-3, 10-1 Interdisciplinary nature

of nanoscale science, engineering, and technology, 12-12 of nanoscience and technology, 10-1 of nanotechnology, 6-3–6-5 of pre-college NSET programs, 12-12 International Association of Nanotechnology, 5-3 International Collaboration of Publications (ICP), 17-7 International Organization for Standardization (ISO), 2-12 International Patent Classification (IPC), 1-8, 1-9 International Union of Crystallography (IUCr), 14-1, 14-5 Interpersonal skills, 5-7 Intrapersonal skills, 5-7 In vitro testing, 2-9, 2-10 IPC, see International Patent Classification (IPC) Iridescent Learning, 5-3, 5-4 ISO, see International Organization for Standardization (ISO) IUCr, see International Union of Crystallography (IUCr)

J Jellium model, 18-11, 18-16, 18-17 Jerome Fisher Program, 5-7 ‘Jmol Crystal Symmetry Explorer,’ 14-15 Jmol/JSmol visualization, 14-12–14-15

K KETs, see Key Enabling Technologies (KETs) Key Enabling Technologies (KETs), 8-3 KICs, see Knowledge and Innovation Communities (KICs) 100kin10, 5-3 Knowledge and Innovation Communities (KICs), 8-4 Knowledge-based innovation, 1-1 Knowledge-transfer process, 13-3 Kondratieff waves, 1-5 K-12 outreach programs, 5-2–5-4, 11-1, 12-1, 12-5, 12-13

L Language-wise distribution of publication, 17-2, 17-3 Latin-American Nanotechnology and Society Network (ReLANS), 15-6 Leadership, in nanotechnology, 5-2, 5-5–5-6, 5-10 Learning progressions, NSET, 12-7 Lesson quality assessment tool (LQAT), 10-7 Liberal Art education approach, 15-4 Liquid quasicrystals (LQC), 18-26, 18-27, 18-28 Living cationic polymerization, 18-26, 18-27, 18-28 LQAT, see Lesson quality assessment tool (LQAT) LQC, see Liquid quasicrystals (LQC)

M Macroscopic application, 7-3 Magic numbers, 3-3, 18-5–18-7, 18-11, 18-13, 18-17, 18-18

I-4 Mendeleev nanoperiodic tables, 18-1–18-2, 18-8, 18-9 for amphiphilic dendrons, 18-19, 18-20–18-21 3-D perspective of, 18-13 ‘Mercury’ program, 14-16 Method of Mercury Porosimetry (MMP), 2-7 Microprocessor, 3-4 Microsoft Windows™, 14-6–14-9 Minimum effective dose, 2-10 MMP, see Method of Mercury Porosimetry (MMP) Modern nanotechnology, 4-2 Molecular Frontiers Foundation, 5-3, 5-10 Molecular nanotechnology, 4-2, 4-4–4-5 Molecular Workbench, 7-2 Multidisciplinary research, 8-2

N Nanobiotechnology bibliographic form of, 17-1, 17-3, 17-10 highly cited publications in, 17-8, 17-9 impact of journals in, 17-8, 17-10 most prolific authors in, 17-5 most prolific countries in, 17-6, 17-7 quantum of literature in, 17-2 scientometric assessment on data analysis, 17-2–17-7 indicators and statistical techniques, 17-3–17-7 measures and indices, 17-7, 17-8 methodology, 17-2 objectives of, 17-2 overview of, 17-1–17-2 “Nano,” concept areas of impact, 3-3–3-4 circular relationships, 3-5 conceptual levels of, 3-4 future of, 3-3 in laser power, 3-1, 3-4, 3-6, 3-9 mega power of, 3-4–3-8 overview of, 3-1–3-3 surface effects, 3-4 trends of, 3-8 NanoCore Ph.D. program, 5-8 Nano-Crystallography group, 14-2, 14-5, 14-9–14-15 NANODYF network, 15-6–15-7 Nanoeducation interdisciplinarity of, 8-2–8-3 Nano2All project dialog methodology, 8-5–8-7 objectives of, 8-4 nanoissues awareness, 8-4 nanotechnology workforce, 8-3–8-4 objectives for, 8-1 NanoEIS, see Nanotechnology Education for Industry and Society (NanoEIS) Nano-electrochemical system devices (NEMS), 9-6 NANOfutures, 8-4, 8-5 nanoHUB.org, 7-2 Nanoissues, 8-4

Index NanoKI, see Nano-Knowledge Instrument (NanoKI) Nano-Knowledge Instrument (NanoKI), 12-9, 12-10 NanoLeap, 9-6, 10-2–10-3, 10-5, 12-8 BSCS Five E approach, 10-10-2 chemistry units, 10-10-3 overview of, 10-10-2 physical science unit, 10-2, 10-3 Nanoliteracy, 7-1–7-2 Nanomaterials, 2-3, 2-4, 3-3, 13-1, 15-7 administration modality, 2-9 application of, 2-2, 2-3 biological/toxicological properties of, 2-5, 2-8–2-11 characterizing properties of, 2-5 classification of, 2-5, 9-6–9-7 conventional methods, 4-4 European regulations on, 2-11–2-12 fundamental properties of, 2-5–2-8 innovations and applications of, 9-3–9-4 on microbiome, 2-4 on microbiota, 2-4, 2-9 parameters, 2-5, 2-8 properties of, 2-2, 2-5 safety evaluation of, 4-4 “Nanomaterials characterization,” 9-7 Nanomatter, 2-4–2-5 techniques to assess biological/toxicological properties, 2-5, 2-8–2-10 characterizing properties, 2-5 fundamental properties, 2-5–2-8 Nanomedicine, 2-2, 4-4 Nanometer, 3-3, 4-2 Nanometric scale, 2-2–2-4 Nanoparticles, properties of, 9-7 Nanoquest 3D, 7-2 Nano Quiz, 12-9, 12-10 Nanoscale characteristics of, 11-5 delivery systems, 1-6–1-7 elemental analog, 18-1 particles, 4-6 surface-area-to-volume relationships, 12-6 Nanoscale atom mimicry; see also Atom mimicry/superatoms, nanoscale hard superatoms, 18-10–18-13 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 soft superatoms, 18-13, 18-14 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 synthesis process, 18-14, 18-15–18-17 Nanoscale science and technology (NST), 9-1 essential concepts of, 9-2, 9-4, 9-8, 9-9 expose school students to, 9-1–9-2 the making of, 9-8 nanomaterials classification of, 9-6–9-7, 9-9 innovations and applications of, 9-3–9-4

I-5

Index programs, 9-2–9-3 characterization methods, 9-5–9-6 fabrication approaches, 9-7–9-9 functionality, 9-6, 9-9 size and scale, 9-4–9-5 size-dependent properties, 9-3, 9-9 Nanoscale science, engineering, and technology (NSET) big ideas in, 12-1–12-2 design-based research, 12-11–12-13 educational significance of, 12-12 interdisciplinary nature of, 12-12 learning research across multiple big ideas, 12-9–12-10 forces and interactions, 12-7–12-8 on nature of matter, 12-7 self-assembly, 12-8 on size and scale, 12-3–12-5 on size-dependent properties, 12-5–12-6 teachers’ pedagogical content knowledge, 12-10–12-11 tools and instrumentation, 12-8, 12-9 mechanistic thinking, 12-12 overview of, 12-1 for pre-college classrooms, 12-1–12-3 recommendations for, 12-12–12-13 Nanoscience and technology (NST) DESI strategies, 10-5, 10-6 in high school science continuous integration of, 10-5–10-8 as curriculum insertion, 10-6–10-7 modular approach to, 10-2–10-5 NanoLeap, 10-2–10-3, 10-5 NanoSense project, 10-3, 10-4 NanoTeach Project, 10-2, 10-5–10-7 overview of, 10-1–10-2 teachers’ views on, 10-7–10-8 interdisciplinary nature of, 10-1 stand-alone integration, 10-8 for students, 10-9 transdisciplinary nature of, 10-1, 10-8 Nanoscience education; see also Nano-Tech Science Education (NTSE) challenges of big ideas, 6-1–6-2 graduate education, 6-3 material behaviors, 6-2 precollege education, 6-2 undergraduate nanoeducation, 6-2–6-3 cutting-edge science and nanotechnology, 13-6, 13-7 digital tools in, 13-3 ethical issues of, 6-4 experiential learning, 13-3–13-4, 13-5 interdisciplinary opportunities of, 6-3 laboratory experiences in, 6-3–6-4 in museums and science center, 6-5 nanotechnology, 13-2–13-3 on-line Virtual Lab, 13-8, 13-9

overview of, 13-1–13-2 scientific experiments in, 13-4, 13-5, 13-6 teacher preparation, 6-5 underrepresented minorities in, 6-4–6-5 virtual experiments in, 13-5, 13-6 Nanoscience evolution, 3-5–3-6 NanoSense project, 10-3, 10-4 NanoSim, 7-2, 7-3 students’ interaction with, 7-3–7-9 benefits and risks, 7-7–7-8 cognitive and learning dimensions, 7-4–7-7 methods, 7-3–7-4 theoretical underpinning, 7-3 usability survey, 7-8–7-9 NanoSim-PC access to, 7-11–7-12 interactive scenarios, 7-9, 7-10 nanoeducation interventions, 7-9, 7-11 nanoscience, 7-11 nanotechnology, 7-11 program structure, 7-9, 7-10 simulations and models, 7-9, 7-11 system requirements, 7-11–7-12 Nanostructured systems, 15-3 NanoTeach Project, 10-2, 10-5–10-7 Nanotechnology, 2-1–2-2, 5-1–5-2, 6-1, 13-1–13-2 achievements account for, 1-6 action plan for, 8-1, 8-4 applications, 9-4 in Aymara, 15-7 bibliometrics analysis, 16-2, 16-3 in biology, 4-2 biomimetic materials, 1-6 in chemistry, 4-2 cutting-edge science and, 13-6, 13-7 definition of, 4-2 development of, 5-3, 5-5 diffusion of, 16-1 educational programs in, 5-2–5-5, 13-2–13-3 emergence of, 16-1–16-2 and entrepreneurship, 1-7–1-11 and environmental risks, 4-6 ethical issues of, 6-4 firms in, 1-10–1-11 future of, 4-3–4-4 graphene, 16-4–16-5 historical path of, 1-6, 4-2 impact on society, 4-3 in indigenous languages, 15-7 industrial outcome of, 1-1–1-2, 1-10, 1-11 and innovation case studies of, 1-6–1-7 definition of, 1-2 for entrepreneurship, 1-2–1-3 linear model of, 1-3 mechanisms of, 1-2 nanotechnologies and, 1-1–1-2

I-6 Nanotechnology (cont.) types of, 1-3 integration of, 10-1 interdisciplinary nature of, 6-3–6-5, 15-3 leadership in, 5-2, 5-5–5-7 the making of, 9-8 NanoSim-PC, 7-11 opportunities for, 4-4–4-5 overview of, 4-1–4-2 pan-industry nature of, 1-5 patenting in, 1-8–1-10 physical phenomena of, 13-10 in physics, 4-2 polymer-based nanocomposite materials, 1-6 production of, 15-3 quantitative approach, 16-2–16-3 in Quechua, 15-7 research in, 1-5, 1-6 responsible research and innovation in, 8-4, 8-5 role models in, 5-10 science mapping, 16-3 scientific literature on, 1-5–1-6 small-world effect, 16-3, 16-8 toxicity risks, 4-6 training, 8-7 in developed and developing nations, 5-9–5-10 for future leaders, 5-6–5-8, 5-10 trends, 4-5–4-6 in United States, 5-1, 5-2, 5-9 workforce for, 4-3, 8-3–8-4 Nanotechnology and Nanobiomimicry, 13-10 Nanotechnology Education for Industry and Society (NanoEIS), 8-3 Nanotechnology Informal Science Education Network (NISE-NET), 6-4, 6-5 Nanotechnology & Molecular Engineering, 5-8 Nano-Tech Science Education (NTSE), 13-6–13-12; see also Nanoscience education Nano2All project, 8-4–8-5 dialog methodology, 8-5–8-7 objectives of, 8-4 Nanowires, 1-5 Nano-world, 13-4 National Institutes of Health (NIH), 14-9 National Nanotechnology Initiative (NNI), 5-1, 8-1, 8-3, 8-4, 15-5 National Science Foundation (NSF), 1-5, 1-6, 14-3, 18-9, 18-18, 18-19 Nature of matter, NSET, 12-7 Nature of science (NOS), 9-8 NEMS, see Nano-electrochemical system devices (NEMS) Next Generation Science Standards (NGSS), 11-1, 11-3 crosscutting concept of, 11-1–11-3 performance expectation, 11-3 sense-making phenomena, 11-3 NGSS, see Next Generation Science Standards (NGSS)

Index NHQ, see Number of high-quality papers (NHQ) NIH, see National Institutes of Health (NIH) NISE-NET, see Nanotechnology Informal Science Education Network (NISE-NET) NNI, see National Nanotechnology Initiative (NNI) Non-traditional intrinsic luminescence (NTIL), 18-31, 18-32, 18-33 NOS, see Nature of science (NOS) NSF, see National Science Foundation (NSF) NST, see Nanoscale science and technology (NST) NTIL, see Non-traditional intrinsic luminescence (NTIL) NTSE, see Nano-Tech Science Education (NTSE) NTSE repository, 13-9 NTSE Virtual Lab, 13-6, 13-8–13-10 Number of high-quality papers (NHQ), 17-6

O Ocean Discovery Institute, 5-2, 5-3 OECD, see Organization for Economic Cooperation and Development (OECD) Omni Nano, 5-2–5-4 On-line Virtual Lab, 13-8, 13-9 Open-access sources for crystallographic information framework files, 14-5–14-6 of 3D print files for bioscientific and biomedical applications, 14-9 Open innovation, 1-8 Organizational schema nanoscale characteristics bumpy as a teaching lens, 11-5–11-6 shaky as a teaching lens, 11-6 sticky as a teaching lens, 11-6, 11-7 scale worlds chart of, 11-3, 11-4 flexibility, 11-5 forces and instruments, 11-4–11-5 objects, 11-4 Organization for Economic Cooperation and Development (OECD), 1-3, 1-9, 1-10, 2-11, 2-12

P PAMAM dendrimers, see Poly(amidoamine) (PAMAM) dendrimers Pan American Development Foundation, 5-3 Patent classification, 1-8–1-10 PCK, see Pedagogical content knowledge (PCK) PCS, see Photon Correlation Spectroscopy (PCS) ‘Peak of Inflated Expectations,’ 14-5 Pedagogical content knowledge (PCK), 6-2, 12-11 Photon Correlation Spectroscopy (PCS), 2-6 Photovoltaic industry, 15-3 Physical confinement mechanism, 18-31, 18-33 PIC, see Polyion complex (PIC) ‘Plateau of Productivity,’ 14-4, 14-5

I-7

Index Poly(amidoamine) (PAMAM) dendrimers, 18-14–18-17, 18-30, 18-31 Polyion complex (PIC), 18-6, 18-7, 18-29, 18-30 Polymer-based nanocomposite materials, 1-6 Portland State University (PSU), Nano-Crystallography group, 14-2, 14-5, 14-9–14-15 Potential curriculum organizational schema nanoscale characteristics bumpy as a teaching lens, 11-5–11-6 shaky as a teaching lens, 11-6 sticky as a teaching lens, 11-6, 11-7 scale worlds chart of, 11-3, 11-4 flexibility, 11-5 forces and instruments, 11-4–11-5 objects, 11-4 “Powers of ten,” 9-4–9-5 Precise polymer synthesis, 18-26–18-28 Pre-college NSET programs, 12-1–12-3 big ideas in, 12-1–12-2 design-based research, 12-11–12-13 educational significance of, 6-2, 12-12 interdisciplinary nature of, 12-12 learning-related research across multiple big ideas, 12-9–12-10 forces and interactions, 12-7–12-8 on nature of matter, 12-7 self-assembly, 12-8 on size and scale, 12-3–12-5 on size-dependent properties, 12-5–12-6, 12-12 teachers’ pedagogical content knowledge, 12-10–12-11 tools and instrumentation, 12-8, 12-9 mechanistic thinking, 12-12 overview of, 12-1 recommendations for, 12-12–12-13 Principle of continuity, 13-4 Principle of interaction, 13-4 “Processing power,” 3-5 Project Exploration, 5-2, 5-3 Project NANO, 9-5 Pseudo-haptic feedback, 7-6 ‘PSU 3D Converter,’ 14-11–14-16 PZ, see Zeta Potential (PZ)

Q QBBs, see Quantized building blocks (QBBs) QELS, see Quasi-elastic Light Scattering (QELS) Quantized building blocks (QBBs), 18-1, 18-3–18-9 Quasi-elastic Light Scattering (QELS), 2-6

R Radical innovation, 1-3 Regeneron Science Talent Search, 5-3, 5-4 Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH), 2-11

ReLANS, see Latin-American Nanotechnology and Society Network (ReLANS) Relative Growth Rate (RGR), 17-3, 17-4 Relative Quality Index (RQI), 17-6 Renaissance Engineering Programme (REP), 5-7, 5-8 REP, see Renaissance Engineering Programme (REP) Responsible research and innovation (RRI), 8-4, 8-5 RGR, see Relative Growth Rate (RGR) RQI, see Relative Quality Index (RQI) RRI, see Responsible research and innovation (RRI)

S Scale world chart, 11-2–11-4 Scanning electron microscope (SEM), 2-6, 9-5, 9-6, 12-9 Scanning Force Microscopy (SFM), 2-6 SCCS, see Scientific Committee on Consumer Safety (SCCS) SCHEER, see Scientific Committee on Health, Environmental and Emerging Risks (SCHEER) School students, to NST, 9-1–9-2 essential concepts of, 9-2, 9-4, 9-8, 9-9 expose school students to, 9-1–9-2 nanomaterials, 9-3–9-4, 9-6–9-7 programs, 9-2–9-3 characterization methods, 9-5–9-6 fabrication approaches, 9-7–9-9 functionality, 9-6, 9-9 innovations and applications, 9-3–9-4 size and scale, 9-4–9-5 size-dependent properties, 9-3, 9-9 Science mapping, 16-3 Science, Technology, Engineering, and Mathematics (STEM), 12-1, 14-1 Scientific and technological dissemination, 15-2–15-6 Scientific Committee on Consumer Safety (SCCS), 2-12 Scientific Committee on Health, Environmental and Emerging Risks (SCHEER), 2-12 Scientific experiments, 13-4, 13-5, 13-6 Scientific skills, 5-7 Scientometric assessment on nanobiotechnology data analysis, 17-2–17-7 indicators and statistical techniques, 17-3–17-7 measures and indices, 17-7, 17-8 methodology, 17-2 objectives of, 17-2 overview of, 17-1–17-2 Scopus databases, 16-3, 16-5, 17-1–17-3 Screening dosages, 2-9–2-10 SE3D, 5-3, 5-4 Self-assembly, NSET, 12-8 SEM, see Scanning electron microscope (SEM) Senior Science Research Seminar, 10-7–10-8 SFM, see Scanning Force Microscopy (SFM) Shell-filling phenomena, 18-1, 18-10, 18-24 Silver nanoparticles, 4-3

I-8 Single-walled carbon nanotubes (SWNTs), 1-11 Size and scale, NSET conceptualizations of, 12-3–12-4 relative and absolute rankings, 12-4 teachers’ conceptions of, 12-4–12-5 Size-dependent properties for fluorescent, 18-16 nanoscale science and technology, 9-3, 9-9 nanoscale science, engineering, and technology, 12-5–12-6 Small-world effect, 16-3, 16-8 SNA, see Social network analysis (SNA) S.NET 2016 conference, 8-5, 8-6 Social network analysis (SNA), 16-2–16-3, 16-8 Soft superatoms, 18-1, 18-3, 18-13, 18-14 engineering CNDPs of, 18-7, 18-29, 18-30, 18-31 vs. hard superatoms, 18-14, 18-15–18-17 Mendeleev periodic tables, 18-31, 18-32 nanoelement categories, 18-19, 18-22, 18-26–18-29 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 Spherical nucleic acids, 18-28, 18-30 SSI, see Sustainable Sciences Institute (SSI) Stand-alone integration, 10-8 Staphylococcus aureus, 12-9 State science standards, 11-1 STEM, see Science, Technology, Engineering, and Mathematics (STEM) Superatom building block, 18-22, 18-25, 18-34 Superatoms/atom mimicry, 18-10, 18-13; see also Atom mimicry/superatoms, nanoscale Supramolecular dendrimers, 18-20 Surface area analysis, 2-7 Surface-area-to-volume relationships, 9-3, 12-6 Sustainable Nanotechnology Organization, 5-3, 5-5 Sustainable Sciences Institute (SSI), 5-9–5-10 Swiss-African Research Cooperation model (SARECO.org), 5-10 SWNTs, see Single-walled carbon nanotubes (SWNTs) Symmetry breaking, 18-4

T Teachers’ pedagogical content knowledge, NSET, 12-10–12-11 Technology transfer, 1-8 TEM, see Transmission Electron Microscopy (TEM) Tensors, 14-6, 14-8, 14-9 “There is Plenty of Room at the Bottom,” 4-2 3D printing in STEM ball-and-stick model, 14-11 ‘Classroom 3D printing,’ 14-5 for atomic level crystal structure, 14-9–14-15 for biomedical applications, 14-9 for bioscientific applications, 14-9 crystallographic information framework files, 14-5–14-6 for crystal structure models, 14-15–14-16

Index ‘Jmol Crystal Symmetry Explorer,’ 14-15–14-16 Microsoft Windows™, 14-6–14-9 print-out times, 14-5 education at college level, 14-3–14-5 Gartner’s hype cycle for, 14-3 interactive creation of, 14-9–14-15 literature overview of, 14-2–14-3 for molecular structure, 14-6–14-9 overview of, 14-1–14-2 technology review, 14-17 visualizations of caffeine molecule, 14-7 ecstasy molecule, 14-6 left-α-quartz single crystal, 14-9 myoglobin molecule, 14-7 quartz contact twin, 14-8 right-α-quartz single crystal, 14-7–14-8 sucrose molecule, 14-6, 14-7 Tiara-like nanoclusters, 18-24, 18-25 Toshiba/NSTA ExploraVision, 5-3, 5-4 Training, in nanotechnology Bloom’s taxonomy approach, 15-4–15-5 developed and developing nations, 5-9–5-10 for future leaders, 5-6–5-8, 5-10 importance of, 15-2 commercial and business reasons, 15-3 social motivations, 15-3–15-4 NANODYF network, 15-6–15-7 overview of, 15-1–15-2 scientific and technological, 15-2–15-6 Transana software, 7-4 Transdisciplinary research, 8-2, 10-1 “Transitional concepts,” 9-6 Transmission Electron Microscopy (TEM), 2-6 2D-1 phenotype adherent cell model, 2-8, 2-10

U Undergraduate nanoeducation, 6-2–6-3 Unifying nanoscience atom mimicry, 18-10, 18-25, 18-33 (see also Atom mimicry/superatoms, nanoscale) central dogma for, 18-1, 18-3, 18-19 critical hierarchical design parameters, 18-3, 18-4 (see also Mendeleev nanoperiodic tables) atom mimicry/superatoms, 18-10 hierarchical transfer of, 18-4–18-8 periodic properties on, 18-8–18-9 dendrimers, 18-7 atom mimicry, 18-10 cluster using, 18-23, 18-24 monomer shell, 18-11, 18-18 poly(amidoamine), 18-14–18-17, 18-30, 18-31 to polyion complexes, 18-7, 18-29, 18-30 supramolecular, 18-20 Terahertz radiation generators, 18-30–18-31 Yamamoto-type, 18-14 hard superatoms, 18-1, 18-2, 18-10–18-13

I-9

Index nanoperiodic roadmap, 18-21–18-26 roadmap of, 18-18–18-19 synthesizers, 18-15 taxonomy of, 18-17–18-18 magic numbers, 18-5–18-7, 18-11, 18-13, 18-17, 18-18 Mendeleev nanoperiodic tables, 18-1–18-2, 18-8, 18-9 for amphiphilic dendrons, 18-19, 18-20–18-21 3-D perspective of, 18-13 nanoperiodic framework for hard superatom roadmap, 18-21–18-26 soft superatom categories, 18-26–18-29 overview of, 18-1–18-3 quantized building blocks, 18-4–18-21 soft superatoms, 18-1, 18-3, 18-13, 18-14 engineering CNDPs of, 18-7, 18-29, 18-30, 18-31 vs. hard superatoms, 18-14, 18-15–18-17 Mendeleev periodic tables, 18-31, 18-32 nanoelement categories, 18-19, 18-22, 18-26–18-29 roadmap of, 18-18–18-19 taxonomy of, 18-17–18-18 systematic framework for, 18-3–18-4 UNITAR, see United Nations Institute for Training and Research (UNITAR) United Nations Institute for Training and Research (UNITAR), 8-4, 8-7 United States Feynman’s stature, 4-2 international collaboration of, 1-9, 17-6, 17-7 nanoeducation in, 8-1 nanotechnology in, 5-1, 5-2, 5-9 educational programs in, 5-2–5-5 industrial outcome of, 1-10 National Nanotechnology Initiative, 5-1, 8-1, 8-3, 8-4 Next Generation Science Standards, 11-1 United States Patent and Trademark Office (USPTO), 1-8, 1-9 Unpacked unit cell, 14-11 USPTO, see United States Patent and Trademark Office (USPTO)

V van der Waals force, 4-2–4-3 VAS, see Visual analysis scale (VAS) Virtual experiments, in nanoscience education, 13-5, 13-6 Virtual nanoworld to expose students, 7-3 on attitudes, 7-3 benefits and risks, 7-7–7-8 cognitive and learning dimensions, 7-4–7-7 methods, 7-3–7-4 usability survey, 7-8–7-9 interactive visualization, 7-1–7-3 NanoSim, 7-2, 7-3 NanoSim-PC program, 7-9–7-12 overview of, 7-1 Visual analysis scale (VAS), 7-4, 7-8 Visualizations of 3D printing in STEM caffeine molecule, 14-7 ecstasy molecule, 14-6 left-α-quartz single crystal, 14-9 myoglobin molecule, 14-7 quartz contact twin, 14-8 right-α-quartz single crystal, 14-7–14-8 sucrose molecule, 14-6, 14-7 VOSviewer, 16-4–16-6, 16-8

W Web of Science (WoS), 16-2–16-4, 16-8 WinTensor software, 14-9 WinXMorph program, 14-7–14-8 WIPO, see World Intellectual Property Organisation (WIPO) World Intellectual Property Organisation (WIPO), 1-8 WoS, see Web of Science (WoS)

Y Yamamoto-type dendrimer, 18-14

Z Zeta Potential (PZ), 2-6

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