(Sackler NAS Colloquium) Nanoscience 030908444X, 9780309084444, 9780309570336

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Nanoscience: Underlying Physical Concepts and Phenomena

Arthur M.Sackler COLLOQUIA

OF THE NATIONAL ACADEMY OF SCIENCES

National Academy of Sciences Washington, D.C.

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Arthur M.Sackler, M.D. 1913–1987 Born in Brooklyn, New York, Arthur M.Sackler was educated in the arts, sciences, and humanities at New York University. These interests remained the focus of his life, as he became widely known as a scientist, art collector, and philanthropist, endowing institutions of learning and culture throughout the world.

He felt that his fundamental role was as a doctor, a vocation he decided upon at the age of four. After completing his internship and service as house physician at Lincoln Hospital in New York City, he became a resident in psychiatry at Creedmoor State Hospital. There, in the 1940s, he started research that resulted in more than 150 papers in neuroendocrinology, psychiatry, and experimental medicine. He considered his scientific research in the metabolic basis of schizophrenia his most significant contribution to science and served as editor of the Journal of Clinical and Experimental Psychobiology from 1950 to 1962. In 1960 he started publication of Medical Tribune, a weekly medical newspaper that reached over one million readers in 20 countries. He established the Laboratories for Therapeutic Research in 1938, a facility in New York for basic research that he directed until 1983. As a generous benefactor to the causes of medicine and basic science, Arthur Sackler built and contributed to a wide range of scientific institutions: the Sackler School of Medicine established in 1972 at Tel Aviv University, Tel Aviv, Israel; the Sackler Institute of Graduate Biomedical Science at New York University, founded in 1980; the Arthur M.Sackler Science Center dedicated in 1985 at Clark University, Worcester, Massachusetts; and the Sackler School of Graduate Biomedical Sciences, established in 1980, and the Arthur M.Sackler Center for Health Communications, established in 1986, both at Tufts University, Boston, Massachusetts. His pre-eminence in the art world is already legendary. According to his wife Jillian, one of his favorite relaxations was to visit museums and art galleries and pick out great pieces others had overlooked. His interest in art is reflected in his philanthropy; he endowed galleries at the Metropolitan Museum of Art and Princeton University, a museum at Harvard University, and the Arthur M.Sackler Gallery of Asian Art in Washington, DC. True to his oft-stated determination to create bridges between peoples, he offered to build a teaching museum in China, which Jillian made possible after his death, and in 1993 opened the Arthur M.Sackler Museum of Art and Archaeology at Peking University in Beijing. In a world that often sees science and art as two separate cultures, Arthur Sackler saw them as inextricably related. In a speech given at the State University of New York at Stony Brook, Some reflections on the arts, sciences and humanities, a year before his death, he observed: “Communication is, for me, the primum movens of all culture. In the arts…I find the emotional component most moving. In science, it is the intellectual content. Both are deeply interlinked in the humanities.” The Arthur M.Sackler Colloquia at the National Academy of Sciences pay tribute to this faith in communication as the prime mover of knowledge and culture.

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CONTENTS

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PNAS Proceedings of the National Academy of Sciences of the United States of America

Contents

Papers from the Arthur M.Sackler Colloquium of the National Academy of Sciences PERSPECTIVES Emulating biology: Building nanostructures from the bottom up Nadrian C.Seeman and Angela M.Belcher

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Quantum dot artificial solids: Understanding the static and dynamic role of size and packing disorder K.C.Beverly, J.L.Sample, J.F.Sampaio, F.Remacle, J.R.Heath, and R.D.Levine

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COLLOQUIUM PAPERS Segmented nanofibers of spider dragline silk: Atomic force microscopy and single-molecule force spectroscopy E.Oroudjev, J.Soares, S.Arcdiacono, J.B.Thompson, S.A.Fossey, and H.G.Hansma

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Molecular dynamics analysis of a buckyball-antibody complex William H.Noon, Yifei Kong, and Jianpeng Ma

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H3PW12O40-functionalized tip for scanning tunneling microscopy In K.Song, John R.Kitchin, and Mark A.Barteau

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Energetics of nanocrystalline TiO2 M.R.Ranade, A.Navrotsky, H.Z.Zhang, J.F.Banfield, S.H.Elder, A.Zaban, P.H.Borse, S.K.Kulkarni, G.S.Doran, and H.J.Whitfield

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Study of Nd3+, Pd2+, Pt4+, and Fe3+ dopant effect on photoreactivity of TiO2 nanoparticles S.I.Shah, W.Li, C.-P.Huang, O.Jung, and C.Ni

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Entropically driven self-assembly of multichannel rosette nanotubes Hicham Fenniri, Bo-Liang Deng, Alexander E.Ribbe, Klaas Hallenga, Jaby Jacob, and Pappannan Thiyagarajan

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Combining constitutive materials modeling with atomic force microscopy to understand the mechanical properties of living cells Mike McElfresh, Eveline Baesu, Rod Balhorn, James Belak, Michael J.Allen, and Robert E.Rudd

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Designing supramolecular porphyrin arrays that self-organize into nanoscale optical and magnetic materials Charles Michael Drain, James D.Batteas, George W.Flynn, Tatjana Milic, Ning Chi, Dalia G.Yablon, and Heather Sommers

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Nanoscale surface chemistry Theodore E.Madey, Kalman Pelhos, Qifei Wu, Robin Barnes, Ivan Ermanoski, Wenhua Chen, Jacek J.Kolodziej, and John E.Rowe

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Magnetic nanodots from atomic Fe: Can it be done? E.te Sligte, R.C.M.Bosch, B.Smeets, P.van der Straten, H.C.W.Beijerinck, and K.A.H.van Leeuwen

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Distributed response analysis of conductive behavior in single molecules Marc in het Panhuis, Robert W.Munn, Paul L.A.Popelier, Jonathan N.Coleman, Brian Foley, and Werner J.Blau

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Design of protein struts for self-assembling nanoconstructs Paul Hyman, Regina Valluzzi, and Edward Goldberg

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CONTENTS iv

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EMULATING BIOLOGY: BUILDING NANOSTRUCTURES FROM THE BOTTOM UP

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Perspective Emulating biology: Building nanostructures from the bottom up

Nadrian C.Seeman†‡ and Angela M.Belcher§ †Department of Chemistry, New York University, New York, NY 10003; and §Department of Chemistry, University of Texas, Austin, TX 78712 The biological approach to nanotechnology has produced self-assembled objects, arrays and devices; likewise, it has achieved the recognition of inorganic systems and the control of their growth. Can these approaches now be integrated to produce useful systems? We hear continually that nanoscience and nanotechnology are frontier areas. Everyone is aware that nanotechnology and nanoscience involve the construction and analysis of objects and devices that are very small on the macroscopic scale. Nevertheless, if the ultimate feature sizes of nanoscale objects are about a nanometer or so, we are talking about dimensions an order of magnitude larger than the scale exploited by chemists for over a century. Synthetic chemists have manipulated the constituents, bonding, and stereochemistry of vast numbers of molecules on the angstrom scale, and physical and analytical chemists have examined the properties of these molecules. So what is so special about the nanoscale? There are many answers to this question, possibly as many as there are people who call themselves nanoscientists or nanotechnologists. A particularly intriguing feature of the nanoscale is that this is the scale on which biological systems build their structural components, such as microtubules, microfilaments, and chromatin. The associations maintaining these and the associations of other cellular components seem relatively simple when examined by high-resolution structural methods, such as crystallography or NMR—shape complementarity, charge neutralization, hydrogen bonding, and hydrophobic interactions. A key property of biological nanostructures is molecular recognition, leading to self-assembly and the templating of atomic and molecular structures. For example, it is well known that two complementary strands of DNA will pair to form a double helix. DNA illustrates two features of self-assembly. The molecules have a strong affinity for each other and they form a predictable structure when they associate. Those who wish to create defined nanostructures would like to develop systems that emulate this behavior. Thus, rather than milling down from the macroscopic level, using tools of greater and greater precision (and probably cost), they would like to build nanoconstructs from the bottom up, starting with chemical systems. What are the advantages of building from the bottom up? Dense chemical variety is one advantage. Just as the surfaces of cellular components contain many features per unit area, a complex chemical surface can be used as a building block and, in principle, its orientation and position can be controlled. By contrast, top-down methods work on materials with little chemical diversity. A second advantage is the vastness of the chemical scale. Even a picomole of material is nearly 1012 copies. Thus, one can imagine producing complex components that form well defined structural motifs organized over large areas in two dimensions or volumes in three dimensions.

Fig. 1. Formation of a 2D lattice from a junction with sticky ends. X and Y are sticky ends and X′ and Y′ are their complements. Four of the monomers on the left are complexed to yield the structure on the right. DNA ligase can close the gaps left in the complex, which can be extended by the addition of more monomers.

DNA NANOTECHNOLOGY To date, the most successful biomimetic component used for self-assembly has been DNA itself (1). Linear DNA double helices seem to be of limited utility, but one can design synthetic molecules that form stable branched structures, leading to greater structural complexity. Branched DNA molecules can be combined by “sticky-ended” cohesion (2), as shown in Fig. 1. In synthetic systems, sticky ends may be programmed with a large diversity; N-nucleotide sticky ends lead to 4N possible different sequences. Sticky ends of sufficient length cohere by base pairing alone but they can be ligated to covalency. Sticky ends form classic B-DNA when they cohere (3); thus, in addition to the affinity inherent in complementarity, sticky ends also lead to structural predictability. If the positions of the atoms on one component are known near the sticky end, the atoms of the other component are also known. This situation is usefully contrasted with, say, an antibody and its antigen. Although the antigen-combining site may be known, the orientation of the antigen within it cannot be predicted; it must be determined experimentally in each case. The key static aims of DNA nanotechnology are to use DNA as scaffolding to crystallize biological macromolecules artificially for crystallography (2) and to organize the components of nanoelectronics (4). The first, and likely the second, of these applications entail the assembly of DNA into periodic networks. Thus, the quadrilateral of Fig. 1 would be

This paper results from the Arthur M.Sackler Colloquium of the National Academy of Sciences, “Nanoscience: Underlying Physical Concepts and Phenomena,” held May 18–20, 2001, at the National Academy of Sciences in Washington, DC. Abbreviations: DX, double crossover; 2D, two-dimensional. ‡To whom reprint requests should be addressed. E-mail: [email protected].

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EMULATING BIOLOGY: BUILDING NANOSTRUCTURES FROM THE BOTTOM UP

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most useful if extended to form a two-dimensional (2D) or three-dimensional lattice. The branched junctions shown in Fig. 1 are not rigid enough to use as building blocks for a lattice (5). This problem has been solved by combining two branched junctions to produce DNA doublecrossover (DX) molecules (6, 7), which consist of two double helices fused by strands that cross between them to tie them together.

Fig. 2. Arrays assembled from DX molecules, (a) A two-component array. Two DX molecules (A and B*) are illustrated schematically (a Top). The two helices are drawn as rectangles, and the complementary sticky ends are represented by geometrical shapes. A is a conventional DX molecule, but B* contains a DNA hairpin protruding from the plane. Below these molecules is an array that shows the two components fitting together to tile a plane, (b) A four-component array. The same conventions apply as in a. This array uses four tiles, A, B, C, and D*, where A, B, and C are conventional DX molecules and D* contains a hairpin. The stripes are separated by twice the distance seen in a.

Fig. 2 illustrates two different 2D arrays that have been produced by DX molecules (8). In Fig. 2a, the repeating unit is 2 DX units, and in Fig. 2b, it is 4 DX units. The B* unit in Fig. 2a and the D* unit in Fig. 2b have circles in their centers, representing another helix that is directed out of the plane. This extra helix can serve as a topographic marker for atomic force microscopy. The dimensions of each component are about 4×16 nm. Thus, the extra helices produce stripe-like features every 32 nm in the AB* array, and every 64 nm in the ABCD* array (8). Other DNA motifs have been used to produce nanoscale patterns in 2D. Although the individual branched junction is flexible, well structured DNA parallelograms can be prepared from four of them. DNA parallelograms have been used to produce mesh-works with tunable cavities (9). Likewise, one can prepare triple-crossover (TX) motifs, containing three fused double helices with coplanar axes. These molecules form 2D arrays that can include TX components rotated out of the plane (10). There are several ways to incorporate complexity in DNA arrays. One way is shown in Fig. 2b, where four different units comprise the asymmetric unit of the repeating structure; extension of this approach entails the expense of producing as many components as needed to produce a particular complex pattern in two or three dimensions. Winfree (11) suggested that it is possible to program sticky ends to produce algorithmic assemblies, much like the colored edges of Wang tiles; these tiles form a mosaic in which each tile edge abuts another with the same color. Appropriate sets of Wang tiles can assemble to perform computational operations and to define patterns with much greater complexity than their total number, thereby saving the expense of producing a large number of different tiles. Recently, the feasibility of this approach has been demonstrated in one dimension with triple-crossover molecules, in which a prototype cumulative exclusive OR calculation was executed (12), as shown in Fig. 3. The input and output Boolean values of each step of this calculation are represented by the sticky ends, thus this is a more stringent test of self-assembly than formation of a periodic lattice. The same sticky end on one side of the red answer tiles represents 0, regardless of whether it is on a correct or incorrect tile for a particular position. Despite overall good fidelity, some errors were detected in this experiment. In addition to static structures, DNA can be used to produce nanomechanical devices. An early device (Fig. 4) was based on the transition between right-handed B-DNA and left-handed Z-DNA (13). The system consists of two rigid DX molecules joined by a DNA helix containing a stretch that can undergo the B-Z transition. Yurke et al. (14) have reported a sequence-

Fig. 3. A cumulative XOR calculation. The XOR operation takes two Boolean inputs and produces a 0 if they are the same and a 1 if they are different. Shown in a are blue input tiles, Xi which represent 0 or 1, according to the presence of a particular restriction enzyme site. These have been assembled in a particular order in b. The red tiles in a contain the four Boolean possibilities as sticky ends on their lower helices. The input is connected to the output through the green C1 and C2 tiles. At the end of the self-assembly, one strand that runs through the entire system is ligated together, thereby connecting the input to the output. It is read by partial restriction, followed by a denaturing gel, much like a sequencing reaction.

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EMULATING BIOLOGY: BUILDING NANOSTRUCTURES FROM THE BOTTOM UP

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dependent DNA device that uses branch migration to remove strands from a tweezer-like construct, allowing it to undergo an opening transition. This approach has been used to produce a robust sequence-dependent rotary device (H.Yan, X.Zhang, Z. Shen, and N.C.S., unpublished data). The development of sequence-dependent DNA nanomechanical devices suggests a future for DNA as a controlling element in nanorobotics: N different 2-state devices incorporated in a nanorobotic superstructure could lead to 2N distinct structural states that could be programmed serially to execute mechanical tasks.

Fig. 4. A DNA nanomechanical device based on the B-Z transition. The device consists of two DX molecules connected by a helix (yellow section) that can undergo the B-Z transition. When this occurs, the bottom domain of the right DX molecule swings from the bottom to the top through a rotary motion.

The control over DNA systems seems to be relatively robust. However, to what ends can it be used? The physical properties of DNA are important for understanding biological systems, but their utility in nanoelectronic devices is unproved. To get the maximum use from the organizational capabilities of DNA, it will be necessary to combine DNA with other nanoscale systems, particularly inorganic systems, whose physical properties lend themselves to direct applications. These materials include inorganic nanocrystals and carbon nanotubes, which represent the most exciting potential species to organize in two or three dimensions. The large sizes and abundant functional groups on DNA tiles suggest that multiple functionalities could be attached to a single tile. THE BIOLOGICAL-INORGANIC INTERFACE As noted previously, the use of biological materials offers many advantages over traditional processing methods to construct the next generation of miniaturized electronics devices, particularly including spatial control on the nanometer scale, parallel self-assembly of multiple electronic components on a single device, and correctability. The critical factors in developing a bio-directed self-assembly approach are identifying the appropriate compatibilities and combinations of biological-inorganic materials, synthesis of the appropriate building blocks, and understanding and controlling building block self-assembly processes. In natural biological systems, macromolecules exert exceptional control over inorganic nucleation, phase stabilization, assembly, and pattern formation (15, 16). Biological systems assemble nanoscale building blocks into complex and functionally sophisticated structures with high perfection, controlled size, and compositional uniformity. These materials are typically soft and consist of a surprisingly simple collection of molecular building blocks (i.e., lipids, peptides, and nucleic acids) arranged in complex architectures. For example, proteins from bones, shells, diatoms, and magnetic bacteria can spatially and temporally nucleate inorganic structures from the nanoscopic to the macroscopic scale. In addition, selectivity and recognition at the molecular scale is a critical feature of living systems. Among the best known examples are antibody-antigen interactions. Unlike the semiconductor industry, which relies on serial lithographic processing to construct the smallest features on an integrated circuit, organisms execute their architectural blueprints by using mostly noncovalent forces acting simultaneously and selectively on many molecular components. The exquisite selectivity of complementary biological molecules offers a possible avenue to control the formation of complex structures based on inorganic building blocks such as metal or semiconductor nanoparticles. DNA oligomer-nanocrystal complexes, for example, have been examined as building blocks for more complex two- and three-dimensional structures (17, 18). Nanocrystal-labeled proteins have also been used to label biomolecular substrates with increased sensitivity (19, 20). Self-assembled monolayers have been used to template nanocrystal organization and in some cases, covalently bind semiconductor nanocrystals to metal surfaces (21). “Nanonetworks” have been formed with gold nanoclusters by, using dithiol connectors (22), and with iron oxide, using biotin-streptavidin connectors (23). Specific hydrogen-bonding-directed aggregation between nanocrystals, using alkanethiol-modified DNA base pairs, uracil, and 2,6 diaminopyridine, has also been demonstrated (24). Only very modest binding specificity between the biological molecule and

Fig. 5. Peptide selection for electronic materials. The 1.9×109 random peptide sequences are exposed to the different crystal substrates; nonspecific peptide interactions are removed with extensive washes. The phage that bind are eluted by lowering the pH and disrupting the surface interaction. The eluted phage are amplified by infecting the E. coli ER2537 host, producing enriched populations of phage, displaying peptides that interact with the specific crystal substrate. The amplified phage are isolated, titered, and reexposed to a freshly prepared substrate surface, thereby enriching the phage population with substrate-specific binding phage. This procedure is repeated three to five times to select the phage with the tightest and most specific binding. The DNA of phage that show specificity is sequenced to determine the peptidebinding sequence.

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EMULATING BIOLOGY: BUILDING NANOSTRUCTURES FROM THE BOTTOM UP

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the inorganic substrate can be achieved through these grafting chemistries. These approaches show potential for the control and placement of nanoparticles but they have not exploited the atomic composition and plane-specific recognition that a biomolecule can exhibit for an inorganic phase or the nanostructural control and regularity that biomolecules typically impose on crystal phases and crystallographic orientations.

Fig. 6. Peptide selectivity for patterned GaAs. Fluorescently labeled phage displaying a peptide specifically selected to bind to GaAs bind only to the patterned GaAs nested square pattern on a wafer. The red lines (1 µm across) correspond to GaAs and the black spaces (4 µm across) are SiO2. This peptide-specific binding could also be used to deliver nanocrystals to specific locations.

In principle, biological molecules can be used to control the assembly of inorganic nanostructures and hybrid inorganic/ organic structures while directing them to self-assemble in the desired manner. Thus, the biological molecules and an unlimited number of different types of nanocrystal building blocks can be mixed in the “pot” and then triggered to self-assemble into their superstructures. To develop the interactions of biological molecules for inorganic materials, biological selection has been used with the goal of synthesizing technologically important inorganic materials to serve as building blocks for new materials. Because nature has not had the opportunity to produce biomolecular interactions with some of the desired materials, A.M.B. and coworkers (25) used phage display to select peptide sequences, and Brown (26) used repeating polypeptides displayed on the surface of the bacterium Escherichia coli to bind selectively to metal particles. To identify the appropriate compatibilities and combinations of biological-inorganic materials, a combinatorial library of genetically engineered M13 bacteriophage was used to select peptides rapidly that could not only recognize but also control the growth of specific inorganic materials (Fig. 5). The phage display library is based on a combinatorial library of random peptides of a given length (e.g., 7- or 12-mers) that are fused to the pIII minor coat protein of the filamentous coliphage M13 (New England Biolabs, www.neb.com). Five copies of the fused random pIII coat protein are located on one end of the phage particle and account for 10–16 nm of the 1 µm viral particle. The library used for the selection consists of 1.9×109 random sequences. This peptide combinatorial approach was used to identify proteins that specifically bind to inorganic nanoparticles such as semiconductor nanocrystals. Hence, this is a promising approach for selecting peptides that can recognize specific inorganic crystals and crystallographic orientations of nonbiological origin, including InP, GaAs, and Si (ref. 25; Fig. 6), and can control II-VI (ZnS, CdS, CdSe, ZnSe, and PbS; A.M.B., unpublished data) semiconductor nanocrystal size, crystal structure, shape, and optical properties. ISSUES AND PROSPECTS Biomimetic nanotechnology holds great promise as a vehicle to achieve progress in the areas of macromolecular crystallography, nanoelectronics, and nanorobotics. The key issues in the area currently include the following: (i) Can control be obtained over the sizes of crystalline or aperiodic arrays? Can 2D crystals be made larger than their current dimensions on the order of a micrometer or two? (ii) Can crystals be produced that are nearly error-free? Applications in nanoelectronics will require high degrees of perfection, although error-tolerant techniques (27) may alleviate this problem. (iii) Can nanomechanical devices transmit forces in the same way that larger devices do in the macroscopic world? (iv) Can the systems described here and in the related experiments pioneered by Alivisatos et al. (17), Mirkin et al. (18), and Mallouk and coworkers (28) be used to interface the architectural prowess of “wet” biomimetic nanotechnology with the functional potency of the “dry” nanotechnology of nanotubes (29) and nanoelectronic components (30, 31)? The successes enjoyed by Whitesides and his coworkers (32) working on a slightly larger scale may be taken as an indication that bottomup assembly and organization can be extended conveniently down through the nanoscale. Current efforts to extend DNA nanotechnology from two- to three-dimensional (3D) arrays can be expected to produce true 3D integration of nanoelectronic components, although this success, when it comes, will lead to other problems of addressing and heat dissipation. Biomolecular recognition and peptide evolution will be used to develop molecular tool kits for the design and synthesis of inorganic nanocrystals with the potential to offer even greater flexibility in materials synthesis and assembly than with current synthetic routes. Biomimetic nanotechnology is just beginning to bloom—the full inflorescence promises to be spectacular. This work has been supported by the National Institute of General Medical Sciences Grant GM-29554 (to N.C.S.), the Office of Naval Research Grant N00014–98–1–0093 (to N.C.S.), the Defense Advanced Research Planning Agency/National Science Foundation Grant CCR-97–25021 (to N.C.S.), National Science Foundation Grants CTS-9986512, EIA-0086015, and CTS-0103002 (to N.C.S.), the Army Research Office Grant DADD19–99– 0155 (to A.M.B.), and by the Welch Foundation (A.M.B.). 1. Seeman, N.C. (1999) Trends Biotechnol. 17, 437–443. 2. Seeman, N.C. (1982) J. Theor. Biol. 99, 237–247. 3. Qiu, H., Dewan, J.C. & Seeman, N.C. (1997) J. Mol. Biol. 267, 881–898. 4. Robinson, B.H. & Seeman, N.C. (1987) Protein Eng. 1, 295–300. 5. Petrillo, M.L., Newton, C.J., Cunningham, R.P., Ma, R.-I., Kallenbach, N.R. & Seeman, N.C. (1988) Biopolymers 27, 1337–1352. 6. Fu, T.-J. & Seeman, N.C. (1993) Biochemistry 32, 3211–3220. 7. Li., X., Yang, X., Qi, J. & Seeman, N.C. (1996) J. Am. Chem. Soc. 118, 6131–6140. 8. Winfree, E., Liu, F., Wenzler, L.A. & Seeman, N.C. (1998) Nature (London) 394, 539–544. 9. Mao, C., Sun, W. & Seeman, N.C. (1999) J. Am. Chem. Soc. 121, 5437–5443. 10. LaBean, T.H., Yan, H., Kopatsch, J., Liu, F., Winfree, E., Reif, J.H. & Seeman, N.C. (2000) J. Am. Chem. Soc. 122, 1848–1860. 11. Winfree E. (1996) in DNA Based Computing, eds. Lipton, E.J. & Baum, E.B. (Am. Math. Soc., Providence, RI), pp. 199–219. 12. Mao, C., LaBean, T.H., Reif, J.H. & Seeman, N.C. (2000) Nature (London) 407, 493–496. 13. Mao, C., Sun, W., Shen, Z. & Seeman, N.C. (1999) Nature (London) 397, 144–146. 14. Yurke, B., Turberfield, A.J., Mills, A.P., Jr., Simmel, F.C. & Neumann, J.L. (2000) Nature (London) 406, 605–608. 15. Belcher, A.M., Wu, X.H., Christensen, R.J., Hansma, P.K., Stucky, G.D. & Morse, D.E. (1996) Nature (London) 381, 56–58. 16. Falini, G., Albeck, S., Weiner, S. & Addadi, L. (1996) Science 271, 67–69.

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17. Alivisatos, A.P., Johnsson, K.P., Peng, X., Wilson, T.E., Loweth, C.J., Bruchez, M.P. & Schultz, P.G. (1996) Nature (London) 382, 609–611. 18. Mirkin, C.A., Letsinger, R.L., Mucic, R.C. & Stofoff, J.J. (1996) Nature (London) 382, 607. 19. Chan, W.C.W. & Nie, S. (1998) Science 281, 2016–2018. 20. Bruchez, M., Moronne, M., Gin, P., Weiss, S. & Alivisatos A.P. (1998) Science 281, 2013–2016. 21. Colvin, V.L., Goldstein, A.N. & Alivisatos, A.P. (1992) J. Am. Chem. Soc. 114, 5221–5230. 22. Baum, T., Bethell, D., Brust, M. & Schiffrin, D.J. (1999) Langmuir 15, 866–871. 23. Li, M., Wong, K.K.W. & Mann, S. (1999) Chem. Mater. 11, 23–26. 24. Cusack, L., Rao, S.N., Wenger, J. & Fitzmaurice, J.D. (1997) Chem. Mat. 9, 624–631. 25. Whaley, S.R., English, D.S., Hu, E.L., Barbara, P.R. & Belcher, A.M. (2000) Nature (London) 405, 665–668. 26. Brown, S. (1992) Proc. Nat. Acad. Sci. USA 89, 8651–8655. 27. Heath, J.R., Keukes, P.J., Snider, G. & Silliams, R. (1998) Science 280, 1716–1721. 28. Mbindyo, J.K.N., Reiss, B.D., Martin, B.R., Keating, C.D., Natan, M.J. & Mallouk, T.E. (2001) Adv. Mater. 13, 249–254. 29. Colbert, D.T., Zhang, J., McClure, S.M., Nikolaev, P., Chen, Z., Hafner, J.H., Owens, D.W., Kotula, P.G., Carter, C.B., Weaver, J.H., et al. (1994) Science 266, 1218–1222. 30. Reed, M.A., Zhou, C., Muller, C.J., Burgin, T.P. & Tour, J.M. (1997) Science 278, 252–254. 31. Collier, C.P., Wong, E.W., Belohradsky, M., Raymo, F.M., Stoddart, J.F., Keukes, P.J., Williams, R.S. & Heath, J.R. (1999) Science 285, 391–394. 32. Trau, M., Yao, N., Kim E., Xia, Y., Whitesides, G.M. & Aksay, I.A. (1997) Nature (London) 390, 674–676.

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QUANTUM DOT ARTIFICIAL SOLIDS: UNDERSTANDING THE STATIC AND DYNAMIC ROLE OF SIZE AND PACKING DISORDER

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Perspective Quantum dot artificial solids: Understanding the static and dynamic role of size and packing disorder K.C.Beverly*, J.L.Sample*, J.F.Sampaio†, F.Remacle‡, J.R.Heath*, and R.D.Levine§¶ *Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095; †Depto de Fisica, Universidade Federal de Minas Gerais, CEP. 30123–970, Belo Horizonte, M.G., Brazil; ‡Département de Chimie, B6c, Université de Liège, B 4000 Liège, Belgium; and §The Fritz Haber Research Center for Molecular Dynamics, The Hebrew University, Jerusalem 91904, Israel This perspective examines quantum dot (QD) superlattices as model systems for achieving a general understanding of the electronic structure of solids and devices built from nanoscale components. QD arrays are artificial two-dimensional solids, with novel optical and electric properties, which can be experimentally tuned. The control of the properties is primarily by means of the selection of the composition and size of the individual QDs and secondly, through their packing. The freedom of the architectural design is constrained by nature insisting on diversity. Even the best synthesis and separation methods do not yield dots of exactly the same size nor is the packing in the self-assembled array perfectly regular. A series of experiments, using both spectroscopic and electrical probes, has characterized the effects of disorder for arrays of metallic dots. We review these results and the corresponding theory. In particular, we discuss temperature-dependent transport experiments as the next step in the characterization of these arrays. Over the past few years a number of new chemical (1) and physical (2, 3) techniques have emerged for manipulating and tailoring the electrical properties of solids. Certain of the chemical techniques have been enabled by the development of synthetic methods for the preparation of narrow-size distributions of organically passivated metal (4) and semiconductor quantum dots (QDs) (5). For our purposes, we use metal QDs as artificial atoms: We use them as nanoscale building blocks for constructing extended two-dimensional solids, or QD superlattices. The beauty of QD solids is 2-fold: First, many physical parameters such as particle size and size distribution, disorder, interparticle coupling, etc. represent knobs that can be individually controlled. Variation of any of these parameters translates directly into either subtle or dramatic changes in the collective electronic response of the superlattice. Second, a one-electron model of the coupled QDs, augmented by the concept of the Coulomb charging energy (6), turns out to capture much of the physics of the system and thus enables experiment and theory to progress hand-in-hand. It is this versatility in both experiment and theory that can potentially turn these QD superlattices into model systems for achieving a general understanding of the electronic structure of solids. We have not completely established that model system yet, but it is our goal to get there, and the point of this article is to assess where we want to go and to serve as a progress report toward that goal. For a chemist, it is convenient to think of the highest-lying electrons on each dot as the valence electrons of an atom. Two interacting identical dots are thus similar to two equivalent, covalently bonded atoms. Within a QD superlattice, electron transfer between neighboring QDs is a low-energy process. Technically, this means that QDs have low charging energies (7, 8). Metallic QDs of 2–10 nm diameter (102–104 atoms) have size-dependent charging energies (I) in the range of 0.5 to 0.1 eV. By contrast, actual atoms have charging energies of at least several eVs. The idyllic picture of identical “atoms” within a perfect lattice fails to account for what are effectively intrinsic properties of chemically synthesized QDs. For example, any two adjacent dots are unlikely to be truly identical. Even the best chemical preparations (9, 10) for producing size-tunable and narrow particle size distributions still produce particles with finite distribution widths, and those widths translate directly into a distribution of site energies. Thus, in general, any two-particle interaction will have some ionic character, as one dot is always more electronegative than the other. Finally, the superlattice itself is characterized by some amount of packing disorder, which contributes to local variations in interparticle coupling strengths, among other things. When the parameters that govern the electronic structure of QD solids are varied, those solids can undergo macroscopic, collective changes that can be observed by eye (Fig. 1). The phenomena highlighted in Fig. 1 is a transition to an electronically delocalized state that is triggered by an acoustic wave, and this is a type of quantum phase transition. We speak of a quantum phase transition when the nature of the quantum mechanical state of the system changes. The change can be discontinuous, analogous to a first-order phase transition, or it can be a continuous change in the energy in which case one might consider the change to be an isomerization. Fig. 2 is a phase diagram showing a subset of such possible transitions, as represented by a plot of interparticle (exchange) coupling vs. disorder. Other representations, such as disorder vs. temperature, or disorder vs. electric field strength, or any other combination of size-distribution disorder, packing disorder, exchange coupling, particle size, temperature, external field strength, etc. can generate equally rich phase diagrams. All of these parameters are under experimental control and may be quantum mechanically modeled. It is exactly this versatility that makes these systems such interesting models for study. Disorder has become a parameter that we have been increasingly able to quantify, and so we emphasize the role of disorder by choosing it as the abscissa in the phase diagram. Qualitatively, disorder can arise from size distribution widths and/or packing defects. It is effectively a measurement of the dissimilarity of adjacent dots, and so it acts against a collective behavior. Size fluctuations are obviously coupled to packing disorder but, for a given distribution, one can independently vary the packing disorder via compression of an initially self-assembled lattice. A sufficiently narrow size distribution of QDs will spontaneously crystallize into a well-ordered hexagonal phase (11). The ordinate in the phase diagram, , represents the strength of the exchange coupling between adjacent dots. is large or

This paper results from the Arthur M.Sackler Colloquium of the National Academy of Sciences, “Nanoscience: Underlying Physical Concepts and Phenomena,” held May 18–20, 2001, at the National Academy of Sciences in Washington, DC. Abbreviations: QD, quantum dot; SP, surface potential. ¶To whom reprint requests should be addressed. E-mail: [email protected].

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QUANTUM DOT ARTIFICIAL SOLIDS: UNDERSTANDING THE STATIC AND DYNAMIC ROLE OF SIZE AND PACKING DISORDER

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small in comparison to two other energies. One is the charging energy I, which represents the energetic cost of transferring an electron from one dot to another one that already has its complement of electrons. The exchange coupling that moves an electron from one dot to another needs to overcome this cost if it is to be effective. The other relevant energy, ∆α, is the range of fluctuations in the energy, α, of the exchanged electron. Because the valence electron is confined to the dot, ∆α/α is a measure of disorder that arises primarily from the size distribution. is large if it can bridge the local size fluctuations, >∆α. By compressing a QD monolayer one can increase . Because the exchange coupling relies on the overlap of the tails of wave functions that are centered on adjacent dots, scales exponentially with the distance D between the dots. Thus, through compression, can be tuned over a wide range. The transition to a collective behavior, when is sufficiently large, is first order. Our initial observation of an optical signature of this transition (12) could be well reproduced by quantum mechanical computations (13) that allowed for size fluctuations. Since that time we have observed other facets of the phase diagram but much more remains to be done.

Fig. 1. Acoustic wave induced transition to a delocalized state. The pressure wave compresses the array and thereby increases the exchange coupling of the QDs, as discussed in connection with Eq. 1. See ref. 12 for the first experimental report and refs. 13 and 28 for more on the theory. Shown is a videocaptured image of a Langmuir monolayer of 4-nm diameter, pentanethiol passivated Ag QDs that has been compressed, using the mobile barriers of the Langmuir trough, to a point that is just short of the transition from an insulator to a metal. A second Teflon barrier, oriented perpendicular to the principal barriers, was connected mechanically to a speaker, and the speaker was electronically connected to a function generator. A 1-V amplitude, ≈100-Hz square wave function was applied to the speaker and transduced as a mechanical vibration in the second Teflon barrier. The mechanical oscillations corresponded a moving wave of high- and low-pressure oscillations through the monolayer. The silvery components of the monolayer are metallic, whereas the darker, reddish regions are insulating. Other more quantitative measurements have indicated that the metallic regions are, in fact, characterized by a free-electron response in the complex dielectric function—i.e., by a negative valued component of the real part of the dielectric function. This ability to dynamically pattern a macroscopic film as an insulator or a metal is, to our knowledge, unique to these materials.

The quantitative results on the role of compression for an array of Ag QD could be fitted by using an exchange coupling of the functional form [1] R is the radius of the dot, D is the lattice constant, and L is the dimensionless range parameter. For small (2R=35 Å) Ag dots, 1/2L=5.5 and α eV. The range of strong coupling is determined by the value D0/2R=1.2 and beyond this point decreases exponentially. This very same coupling (14) was able to reproduce the experimentally measured effect of lattice compression on the frequency-dependent complex dielectric function (15). In the study of the conductivity (16) reported (see Fig. 5), the dots have twice the radius. Because the exchange coupling depends on the overlap of the wave functions of adjacent dots and so declines exponentially as exp(−κD) where the length scale 1/κ should remain the same, we use 1/2L=11. Also, to keep the strength of the coupling at contact, D/2R=1, the same, we use D0/ 2R=1.1. Some disorder is always present, and, even at room temperature, disorder may mask the Mott insulator to metal transition (6) that occurs when the exchange coupling exceeds the charging energy I. It is easy to assess when this will be the case. Size fluctuations with the resulting variations in the energies of the dots will affect the energy of the ionic bands. When disorder is sufficiently high such that ∆α>I, the lowest ionic band can overlap the covalent band. This happens when it is energetically advantageous to move an electron from a dot of higher energy to one with a lower α. The cost is I and the gain is ∆α. Indeed, it is even possible for the ionic band to be lowest in energy (17, 18). For highly disordered lattices, the effects of the charging energy therefore will be masked and the key reduced variable is /∆α. 0=0.5

Fig. 2. A quantum mechanical phase diagram for an array of QDs with size and packing fluctuations. The full phase diagram is actually much richer than what is shown as there are various other ways to tune the relative energies of the system, as discussed in the text.

Even within the limited context of the phase diagram of Fig. 2, the scope of possible transitions is not exhausted by the first-order transition to a collective, delocalized state or the second-order transition between the covalent and ionic states. In the weak coupling regime where /∆α≤1, the theory suggests that there is another second-order transition, from a localized to a domain localized state (19). In the localized state each electron is defacto confined to a single lattice site; the individual dots are not effectively coupled. It is a strictly insulating state. The domain-localized state has the electrons delocalized over finite QD domains that are smaller than the overall dimensions of the film. This state has recently (19) been imaged by introducing yet another variable, an electric field in the plane of the lattice, and measuring the surface potential (SP) in the x-y plan of the film (20). The domain localized state is not a packing configuration. Fig. 3 shows not only the contours of the SP but also a co-collected topographic image of the QD array, and the images are uncorrelated. Thus, the crystallographic structure of the array can be quite different from the electronic structure. Room temperature SP images, collected as the interdot separation distance (D/2R) is decreased, exhibit a transition to a collective behavior. This was seen as an Ohmic-like monotonic drop in the SP across the array. However, consistent with the theory, this delocalization transition was observed only (20) in arrays characterized by a limited amount of disorder. Highly disordered arrays remained domain localized, even at high compression. Theoretically, we expect that the application of a voltage gradient in the plane of the array will counteract the effects of size disorder. In the presence of an external potential the site energy of the ith dot has the form

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QUANTUM DOT ARTIFICIAL SOLIDS: UNDERSTANDING THE STATIC AND DYNAMIC ROLE OF SIZE AND PACKING DISORDER

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Fig. 3. SP (Right) and corresponding atomic force microscopy images (Left) of the same silver nanoparticle film, 25 microns in width between the electrodes. The SP images scale from 0.0 V to 0.1 V from dark red to pink, and the topography images scale from 0 nm to 350 nm. Image pairs A-C correspond to a voltage gradient of 0.1, 0.2, and 0.3 V respectively. At 0.4 V, the behavior of the SP image is ohmiclike, showing a monotonic gradation. The structures in the SP images are not correlated to any topographical features. For the film shown D/2R= 1.20 for dots of size=70±7 Å. The film resistance is 80 MΩ. There is a bright spot near the right electrode, which corresponds to a hole in the conductive film as observed in the topographic image. [2]

αi is the fractional range of variation in the site energies, α, multiplied by a random number between −1 and 1 so that the site energy of the ith dot in the array is α0(1+ αi). α0Vi is the external potential at the position of the ith dot, computed from the geometry of the array, and e is the charge on the electron. Without an external potential the energies of different dots are uncorrelated. Once a potential is applied, there is a systematic contribution to the site energy.

Fig. 4. The temperature dependence of the experimentally determined resistance for 70-Å Ag nanoparticle monolayer films is shown for three different size distributions of particles. All resistance measurements were taken at low bias (−0.3 V to 0.3 V) within the ohmic regime of the I/V curve. Three different temperature regimes are identified. At high temperatures (above ≈200 K) a metallic behavior is seen. Below TMA, the resistance changes over to a simple activated process where ln(Resistance) ∝ Ea/T. TMA is linearly correlated with Ea. At temperatures lower still, below TAV, a ln(Resistance) ∝ (T0/T)1/2 dependence is seen and is interpreted in terms of a variable range hopping mechanism. TAV moves to lower temperatures with decreasing size distribution.

Adjacent dots are correlated if they are oriented in the direction of the potential gradient while they remain uncorrelated otherwise. When the applied gradient is large enough, the systematic monotonic variation in the site energies can compete with the fluctuations. We confirmed this theoretical prediction by increasing the voltage drop per particle. Thereby one can observe (20) a transition from a domain localized state where there are “islands” of higher SP to stripe-like regions where the stripes are directed along the gradient. Such quantum mechanical computational results are shown in ref. 20. Upon further compression the array exhibits a transition to collective behavior, meaning a monotonic drop of the SP in the direction of the applied bias was observed, and also is clearly evident in the computations. DC transport measurements provide a particularly appealing way to quantify the electronic phase diagram of two-dimensional QD solids in various dimensions, including exchange coupling, temperature, and disorder. Fig. 4 is a representation of the measured resistivity vs. temperature for three different arrays with a small, but finite, size distribution for the dots. Imaging shows these fairly compressed arrays to be regularly ordered in a hexagonal packing. As temperatures drop to about 200 K, such a film will exhibit a decreasing resistance with decreasing temperature, just as any normal metal. At some temperature TMA (180–230 K), the conductivity properties change over to an activated mechanism such that ln(Resistance) ∝ Ea/T. TMA and Ea are linearly correlated, and Ea decreases with increasing lattice compression, implying that these two quantities represent a measurement of the strength of exchange coupling, . At a lower temperature TAV (20–80 K), the transport characteristics again change, this time to a ln(Resistance) ∝ (T0/T)1/2 dependence. Such a T−1/2 dependence is known in the solid-state theory of amorphous conductors (21) as the regime of variable range hopping (VRH). The transition temperature to VRH, TAV is not correlated with Ea, but it does exhibit a strong, linear dependence on the width of the particle size-distribution. Thus, TAV is a measure of disorder. One interesting prediction that we have extracted from our experiments is that, at size distributions widths