EKC 2019 Conference Proceedings: Science, Technology, and Humanity: Advancement and Sustainability [1st ed.] 9789811583490, 9789811583506

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Jong Mun Park Dong Ryeol Whang  Editors

EKC 2019 Conference Proceedings Science, Technology, and Humanity: Advancement and Sustainability

EKC 2019 Conference Proceedings

Jong Mun Park • Dong Ryeol Whang Editors

EKC 2019 Conference Proceedings Science, Technology, and Humanity: Advancement and Sustainability

Vienna, Austria, July 15–18, 2019 Proceedings

Editors Jong Mun Park ams AG Premstaetten, Austria

Dong Ryeol Whang Department of Advanced Materials Hannam University Daejeon, Republic of Korea

ISBN 978-981-15-8349-0 ISBN 978-981-15-8350-6 https://doi.org/10.1007/978-981-15-8350-6

(eBook)

© Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

EKC 2019, the 12th EKC, was held in Vienna, Austria, from July 15 to 18, 2019. It was hosted by the Korean Scientists and Engineers Association in Austria (KOSEAA) together with the Korean Federation of Science and Technology Societies (KOFST) and eight other Korean Scientists and Engineers Associations in Europe which are in Germany (VeKNI), the UK (KSEAUK), France (ASCoF), Austria (KOSEAA), Finland (KOSES), Scandinavia (KSSEA), the Netherlands (KOSEANL), Switzerland (KSEAS), and Belgium (KOSEAbe). Since its first successful start in 2008 in Heidelberg, Germany, EKC has been held annually in different European countries and has become the most important scientific and social event, bringing scientists and engineers from Europe and Korea together. Under the theme of “Science, Technology and Humanity: Advancement and Sustainability,” EKC 2019 was successfully held with more than 680 registered participants. Three hundred twenty nine papers were presented in 50 technical sessions. Among them, high impact-research results from EKC 2019 are selected, peer-reviewed, and stapled in this volume. Fourteen research papers were submitted and 11 were accepted after rigorous peer-review process from the program committee members and external experts. Premstaetten, Austria Daejeon, Republic of Korea January 2020

Jong Mun Park Dong Ryeol Whang

v

Organization

CONFERENCE CHAIR PARK, Jong Mun (ams AG/KOSEAA President) CONFERENCE CO-CHAIRS KIM, Myung-Ja (KOFST President) PARK, Wonsun (GEOMAR Helmholtz Centre for Ocean Research Kiel/VeKNI President) LIM, Sungwoo (The Open University/KSEAUK President) KIM, Junbeum (Université de Technologie de Troyes/ASCoF President) CHO, Hyong Sil (Microsoft/SiLnD/KOSEANL President) CHOE, Young Han (International Telecommunication Union/KSEAS President) LEE, Jae Wung (VTT Technical Research Centre of Finland/KOSES President) YOO, YoonSeon (BlackBerry/KSSEA President) OH, Kun Sang (KOSEAbe President) ADVISORY BOARD LEE, Eun-Woo (KOFST) YOO, Martin S. D. (CRUSE Offshore GmbH/VeKNI) SEOK, Joon-Weon (GORI Engineering/VeKNI) JUN, Chang Hoon (ITER/ASCoF) JEUNG, Gwang-Hi (Institut des sciences moléculaires de Marseille/ASCoF) HAN, Man Wook (Technische Universität Wien/KOSEAA) KIM, Keunjae (SSPA/KSSEA) PARK, Migeun (University of Strathclyde/KSEAUK) SECRETARY GENERAL LEE, Hana (Technische Universität Graz/KOSEAA)

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Organization

REGISTRATION GWON, Jihee (Muthesius University of Fine Arts and Design/VeKNI) KWON, Jaedeok (University of Glasgow/KSEAUK) KANG, Myung-Ah ( Universté Clermont Auvergne/ASCoF) LEE, Sun Mi (KOSEANL) LEE, Juneseung (ETH Zurich/KSEAS) MUN, Gwan-gyeong (Intel/KOSES) KIM, Jaeoh (KSSEA) HEO, Changhoon (imec/KOSEAbe) PROGRAMME CHAIR WHANG, Dong Ryeol (Johannes Kepler Universität Linz/KOSEAA) DIVISION CHAIRS KANG, Kab Seok (Max Planck Institute for Plasma Physics/VeKNI) KIM, Chan (European XFEL/VeKNI) YOON, Songhak (Fraunhofer IWKS/VeKNI) KIM, Wonjae (VTT Technical Research Center of Finland/KOSES) MOK, K. Hun (Trinity College Dublin/KSEAUK) NAM, Kiwoong (Institut National de la Recherche Agronomique/ASCoF) KIM, Junbeum (University of Technology of Troyes, France/ASCoF) LEE, Hyunjung (City of Stuttgart, Office for Environmental Protection/VeKNI) LEE, Pyoung-Jik (University of Liverpool/KSEAUK) SEO, Hyewon (CNRS-Univ. Strasbourg/ASCoF) JUNG, Sung Kyo (NXP Software/ KOSEAbe) CHOI, Jung Han (Fraunhofer Heinrich Hertz Institute/VeKNI) JEONG, Cheol-Ho (DTU (Denmark Technical University)/KSSEA) HA, Kwangtae (Fraunhofer IWES/VeKNI) CHO, Hyong Sil (SiLnD; Microsoft/KOSEANL) LOC CHAIR HAN, Man Wook (Technische Universität Wien/KOSEAA) FINANCE DIRECTOR LEE, Seung-Hun (KOSEAA) LOC MEMBERS PARK, Young-Saeng (University of Warwick/KSEAUK) KOCH, Kyungran (KOSEAA) MIN, Jihoon (IIASA/KOSEAA)

Contents

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Cigánek, Patricie Heinrichová, Martin Weiter, and Jozef Krajčovič

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Machine Learning Approach on Steel Microstructure Classification . . . . 13 Haon Park and Abdullah Öztürk Metabolomics and Its Applications to Personalized Medicine . . . . . . . . . . 25 Lee Sherlock and K. H. Mok Partition-Based Task Mapping for Communication Energy Minimization in 3D Network-on-Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Sanghoon Kwak Design of Floating Offshore Wind Turbine (FOWT) “SelfAligner” . . . . . 55 Jens Cruse, Moustafa Abdel-Maksoud, Alexander Düster, Andreas Bockstedte, Gerrit Haake, and Sönke Siegfriedsen Design of Highly Loaded Slewing Bearings – The Collaborative Project HBDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Jae-Il Hwang, Jan Torben Terwey, Josephine Kelley, Felix Saure, Heinrich Peter Schönemeier, and Gerhard Poll Preliminary Study on Blade Trailing Edge Flap System Using Flexible Torsion Bar and Worm Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Kwangtae Ha Validation of Real Time Gait Analysis Using a Single Head-Worn IMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Tong-Hun Hwang, Julia Reh, Alfred O. Effenberg, and Holger Blume Three-Dimensional Visualization of Atomic Ordering by Bragg Ptychography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Chan Kim and Anders Madsen ix

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles Martin Cigánek, Patricie Heinrichová, Martin Weiter, and Jozef Krajčovič

Abstract The effect of the alkylation and solubilization ethyladamantyl side chains to diketopyrrolopyrroles (DPPs) on the thermal and optical properties was systematically investigated. Nucleophilic substitution of various alkyl chains into the DPP molecule is a powerful tool for increasing solubility and processability of these pigments, leading to their broader applications. Moreover, alkylation can often contribute to solid state packing and structural ordering of DPPs, depending mainly on a character of alkyl chain. In this work, we have synthesized the series containing three derivatives (N,N0 -, N,O0 - and O,O0 -substituted) of alkylated thiophene-DPP derivatives by ethyladamantyl substituents. It was focused on precise separation of all formed DPP derivatives in order to their deeper study. It has been found that the O-substitution leads to worse thermal stability of materials, based on thermogravimetric measurements. On the other hand, ethyladamantyl side chain as rigid alicyclic substituent very effectively contributed to increase of melting point and thermal stability. The new findings provide valuable information about the previously overlooked regioisomers formed during alkylation of the DPP molecule, in particular in terms of thermal stability and optical properties in solution. Keywords Diketopyrrolopyrrole · Alkylation · Adamantyl

1 Introduction In recent years, soluble organic semiconductors have found a number of significant applications in various areas of organic electronics, mostly due to their facile processability in common organic solvents and lower production costs compared to conventional inorganic materials [1]. Organic pigments are the key and wellknown molecules suitable for applications in organic electronics [2, 3]. They are

M. Cigánek · P. Heinrichová · M. Weiter · J. Krajčovič (*) Faculty of Chemistry, Brno University of Technology, Brno, Czech Republic e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 J. M. Park, D. R. Whang (eds.), EKC 2019 Conference Proceedings, https://doi.org/10.1007/978-981-15-8350-6_1

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characterized by high absorption coefficients [2], high thermal and chemical stability [2] and good charge transfer capability [3]. One of the most recently examined and most attractive groups of organic pigments are derivatives of 2,5-dihydropyrrolo [4,3-c]pyrrolo-1,4-dione (DPP) [4], having a considerable application potential for easily processed low-cost sustainable electronics devices [5]. The DPP backbone provides the great potential for a wide range of chemical derivatizations to prepare target derivatives exhibiting the desired chemical and physical properties [6]. As a result, there are currently many significant scientific reports dealing with the applications of DPP derivatives as high performance organic pigments used in functional devices such as organic field-effect transistors [7–11], organic light-emitting diodes [12, 13], dye-sensitized [14] and bulk heterojunction solar cells [15], sensors [16] and biosensors [17], fluorescence probes [18] etc. The DPP derivative substituted with 2-thiophene at positions 3 and 6 is most often found in the previously mentioned fields of application [19]. An important approach to increase efficiency of this material is to extend the length of π-conjugation in the molecule, which can be achieved by polymerization via direct arylation [20, 21] or by the cross-coupling reactions on the side aromatic rings of the DPP core [22, 23]. Another very important derivatization is N,N0 -alkylation of the DPP molecule, providing considerably higher solubility compared to basic N,N0 -unsubstituted DPP derivatives. These derivatives possess good thermo and photostability [2, 24], however strong intermolecular hydrogen bonding between neighbouring oxygen and nitrogen atoms of lactam groups leads to a relatively low solubility in most of common organic solvents [2]. The solubility improvement of N, N0 -alkylated DPPs with linear or branched alkyl chains is caused by interruption of intermolecular hydrogen bonds and leads to broader applications, especially in previously mentioned areas of electronics [25]. On this basis, alkylation is one of the most important and most common derivatization of DPPs. Incorporation of alkyl chains into DPP core is performed through base-catalysed nucleophilic substitution, usually carried out as a one-pot reaction [26]. Nevertheless, two neighbouring Oand N-atoms of the lactam group lead to delocalization of the negative charge in the formation of the DPP anion, resulting to decreasing of regioselectivity of N,N0 -substitutions. The formation of O-alkylated DPP by-products was already proved [27] but there are very few reports dealing with their properties [28, 29]. Nevertheless, O,O0 - and asymmetrical N,O0 - adamantane-substituted derivatives can be a point of interest not only from fundamental but also from practical point of view. Recently the synthesis and study of a novel DPP derivative N,N0 -substituted by bulky ethyladamantyl chains was reported by our group [30]. Substitution has led to extraordinary high ordered adamantane induced molecular packing what resulted to exceptional ambipolar behaviors of this new pigment. During the synthesis of the aforesaid DPP derivative, very low regioselectivity of N-alkylation was encountered and the formation of by-products was largely observed. Therefore, the main aim of this work has been to focus on systematically study of the influence of bulky adamantyl chains on either regioselectivity of the DPP alkylation and properties of the resulting derivatives. The target was to synthesize and isolate all formed products in weighable quantities and subjecting these

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles

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derivatives to the study of the effect of adamantyl substituents on optical and thermal properties in case of symmetrical (N,N0 -; O,O0 -) and asymmetrical (N,O0 -) products.

2 Experimental Section 2.1

Materials

2-Thiophenecarbonitrile (99%), sodium, iron(III) chloride (>97%, anhydrous), tertAmyl alcohol (99%), N,N-dimethylformamide (DMF, 99.8%, anhydrous), potassium carbonate (99.99%, anhydrous) and deuteron chloroform (99.96 atom % D) were purchased from Sigma-Aldrich (now Merck) and were used as received. Diisopropyl succinate (98%) was purchased from Synthesia, Inc. and also was used as received. 1-(2-Bromoethyl)adamantane (98%) was purchased from Provisco CS Ltd. Acetic acid (99%), isopropyl alcohol (p.a.), methanol (p.a.), toluene (p.a.) and chloroform (p.a.) were purchased from PENTA Ltd. and were used as received. Separation by column chromatography was carried out on Silica Gel 60 Å (230–400 mesh, Sigma-Aldrich). All reactions were performed in oven-dried apparatus, under argon atmosphere while magnetically stirred.

2.2 1

Characterization

H NMR spectra were recorded on a FT-NMR spectrometer Bruker Avance III 300 MHz or 500 MHz in CDCl3. Chemical shifts (δ) are given in parts per million (ppm) relative to TMS as an internal reference. The melting point was determined on a Kofler apparatus and the temperature was not calibrated. Mass spectra were recorded on a GC–MS spectrometer Thermo Fisher Scientific ITQ 700 (DEP). Elemental analysis was measured with an elemental analyser Flash 2000 CHNS Thermo Fisher Scientific. Thermogravimetry (TG) was conducted on TA Instruments Q5000IR (New Castle, Delaware, USA) to analyze the stability of the derivatives and changes in mass before degradation. A sample was placed on the Pt crucible sample holder and heated at 10  C/min from room temperature to 600  C under a stream of nitrogen (5.0 ultra high purity) flow rate 40 mL/min. TG was used to analyze the thermal stability of investigated materials. Briefly, the TG record (dependence of mass on temperature) was derived and onset of the derivate was determined. TG records were evaluated using TRIOS and Universal analysis software provided by TA Instruments. Solutions were prepared by diluting materials in anhydrous chloroform. Concentration of materials was 105 to 106 mol dm3. Solutions were characterized in a quartz cuvette (Herasil®, Heraeus Quarzglas Co.). Absorption spectra of samples were measured by employing Varian Cary Probe 50 UV-VIS spectrometer (Agilent

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Technologies Inc.). The fluorescence spectra were recorded with a Horiba Jobin Yvon Fluorolog. This apparatus equipped by integration sphere was also used to determination of fluorescence quantum yield by absolute method. Fluorescence lifetime was measured by Horiba Jobin Yvon Fluorocube.

2.3

Synthesis

The targeted series of alkylated DPP derivatives by ethyladamantyl chains were synthesized according to the known procedure [26, 31]. The synthetic route and the molecular structures are illustrated in the Fig. 1.

S

CN

O

O

+

O

(i)

O

O

N

O

N,N'-EtAd-Th-DPP: 36%

O

S

N

S

N

+

+ S

(ii)

S

S

N

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O N H Th-DPP: 59%

O

O

H N

S

N

O

S

N

O

N,O'-EtAd-Th-DPP: 20% O,O'-EtAd-Th-DPP: 6%

Fig. 1 Synthetic pathway for the synthesis of the basic thiophene DPP molecule and subsequently N,N0 -; N,O0 - and O,O0 -alkylated DPP derivatives (i) Na, FeCl3 (cat.)/t-amyl alcohol, 102  C, 24 h (ii) 1. K2CO3; 2. 1-(2-bromoethyl)adamantane/DMF, 105  C, 2 h

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles

5

Synthesis of 2,5-dihydropyrrolo[4,3-c]pyrrolo-1,4-dione molecules Sodium (~1.3 equiv., 7.2 g, 313.2 mmol) was dissolved in 400 ml of tert-amyl alcohol heated to reflux and with addition of catalytic amount of iron(III) chloride. After dissolution of all sodium, it was added in one portion 1.0 equiv. of thiophene-2carbonitrile (26.2 g, 240.0 mmol) and the reaction mixture was stirred for 30 min at reflux. Then, 0.65 equiv. of diisopropyl succinate (31.6 g, 156.2 mmol) dissolved in 80 ml of tert-amyl alcohol was gradually added dropwise for 4 h and the mixture was stirred at reflux for 18 h. After that, protolysis was performed by addition of diluted acetic acid to the reaction mixture cooled to laboratory temperature. The mixture was refluxed for 6 h and then, heterogenic mixture was filtered while hot and filter cake was washed with hot water and isopropyl alcohol. Crude product was refluxed in methanol for 1 h and after that, it was filtered while hot to get pure product. Th-DPP. Dark purple solid (27.5 g, yield 59%). Melting point >400  C, 1H NMR (300 MHz, DMSO-d6, ppm): δ ¼ 11.21 (s, 2H), 8.20 (d, J ¼ 3.01 Hz, 2H), 7.93 (d, J ¼ 3.04 Hz, 2H), 7.31–7.27 (m, 2H), Anal. calcd. for C14H8N2O2S2: C 55.98%, H 2.68%, N 9.33%, Found: C 55.42%, H 2.37%, N 9.71%. Synthesis of the alkylated DPP derivatives Anhydrous potassium carbonate (~5.3 equiv., 2.4 g, 17.4 mmol) was added to a solution of Th-DPP (1.0 equiv., 1.0 g, 3.3 mmol in 45 ml of anhydrous DMF) and the mixture was heated to 60  C and stirred for 1 h. Then, 1-(2-bromoethyl)adamantane (3.50 equiv., 2.81 g, 11.6 mmol) dissolved in 20 ml of anhydrous DMF was gradually added dropwise for 30 min. After 20 min, the mixture was heated to 105  C and stirred for 2 h. Then, DMF was distilled off by vacuum distillation, solid material was suspended in methanol and filtered to get crude product. N,N’-EtAd-Th-DPP. Violet crystal material (0.75 g, yield 36%) was obtained after purification of the crude product by column chromatography on silica gel (toluene/chloroform 3/1) and the following recrystallization in toluene with addition of n-heptane. Melting point 321  C, 1H NMR (500 MHz, CDCl3, ppm): δ ¼ 8.91 (d, J ¼ 3.8 Hz, 2H), 7.64 (d, J ¼ 4.9 Hz, 2H), 7.27 (dd, J ¼ 6.0, 5.0 Hz, 2H), 4.14–4.11 (m, 4H), 1.99–1.82 (m, 6H), 1.75–1.72 (m, 7H), 1.68–1.65 (m, 19H), 1.53–1.51 (m, 3H), EI [m/z] 624.89, Found 624.97, Anal. calcd. for C38H44N2O2S2: C 73.04%, H 7.10%, N 4.48%, S 10.26%, Found: C 73.15%, H 7.08%, N 4.42%, S 10.38%. N,O’-EtAd-Th-DPP. Dark violet solid material (0.41 g, yield 20%) was obtained after purification of the crude product by column chromatography on silica gel (toluene/chloroform 3/1). Melting point 210  C, 1H NMR (300 MHz, CDCl3, ppm): δ ¼ 8.42 (d, J ¼ 3.9 Hz, 1H), 8.25 (d, J ¼ 3.8 Hz, 1H), 7.69 (dd, J ¼ 5.9, 4.1 Hz, 1H), 7.49 (dd, J ¼ 5.6, 4.1 Hz, 1H), 7.27 (d, J ¼ 3.2 Hz, 1H), 7.24–7.17 (m, 1H), 4.67–4.63 (t, J ¼ 7.8 Hz, 2H), 4.06–4.00 (m, 2H), 1.98 (m, 6H), 1.75–1.67 (m, 26H), 1.62–1.53 (m, 3H), Anal. calcd. for C38H44N2O2S2: C 73.04%, H 7.10%, N 4.48%, Found: C 72.81%, H 7.01%, N 4.57%. O,O’-EtAd-Th-DPP. Dark violet solid material (0.12 g, yield 6%) was obtained after purification of the crude product by column chromatography on silica gel (toluene/chloroform 3/1). Melting point 221  C, 1H NMR (300 MHz, CDCl3,

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ppm): δ ¼ 8.05 (d, J ¼ 4.5 Hz, 2H), 7.55 (d, J ¼ 5.5 Hz, 2H), 7.19 (dd, J ¼ 8.1, 1.1 Hz, 2H), 4.69–4.65 (m, 4H), 1.98 (m, 4H), 1.70–1.64 (m, 24H), 1.54 (s, 4H), 1.34–1.26 (m, 2H), Anal. calcd. for C38H44N2O2S2: C 73.04%, H 7.10%, N 4.48%, Found: C 72.96%, H 6.99%, N 4.51%.

3 Results and Discussion 3.1

Thermogravimetric Analysis

The thermogravimetric record of the N,N0 -EtAd-Th-DPP showed the degradation around 370  C, but it was preceded by a mass loss about 15%. Thermal stability of asymmetrically substituted derivative N,O0 -EtAd-Th-DPP showed degradation at 264  C. The thermogravimetric record of O,O0 -EtAd-Th-DPP derivative showed a continuous mass decrease from 100 to 250  C (mass loss around 20%) and the rate of mass decrease accelerated, which means that sample was degraded. Therefore, correct temperature of the degradation cannot be determined using thermogravimetric analysis (see Fig. 2). It is obvious that N,N0 -EtAd-Th-DPP derivative exhibited by far the highest thermal stability compared to the other two derivatives studied. Therefore, it has been proved that O-substitution results in formation of much less stable derivatives in comparison to the product of N,N0 -alkylation (for summary, see Table 1).

3.2

Optical Properties in Solution

The optical properties of DPP derivatives were studied in solution to investigate the effects given by the position of solubilization group substitution. The Fig. 3 summarizes absorption and fluorescence emission spectra. The origin of fluorescence spectra is confirmed by excitation spectra, which are in good agreement with absorption spectra. There is significant difference between spectra of N,N0 -; N,O0 - and O,O0 -substituted DPPs. Quantitative parameters describing optical properties of solutions are summarized in Table 2. They are confirming that the position of alkyl chain substitution was leading to significant differences in optical properties. Absorption maximum is shifted with change of substitution position, while N, N0 -EtAd-Th-DPP had maximum at 549 nm, N,O0 -EtAd-Th-DPP had maximum at 533 nm and O,O0 -EtAd-Th-DPP at 505 nm (see spectra on Fig. 3 and Table 2). These shifts seem to be mainly consequence of suppression of electron transition to lower vibration modes of excitation states in case of N,O0 - and O,O0 -derivatives (zero phonon transitions are suppressed).

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles

7 1.2

100 404.91°C

Weight (%)

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60

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40 0.2

Deriv. Weight (%°C)

1.0

N,N’-EtAd-Th-DPP

20 0

0

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100

372.51°C 0.0 300 400 500 600

Temperature (°C) 378.65°C

N,O’-EtAd-Th-DPP

0.8 0.6 0.4

60 296.50°C

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0.4 80

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344.17°C

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0 0

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120

Weight (%)

100

Weight (%)

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Deriv. Weight (%°C)

120

–0.2 800

Temperature (°C)

Temperature (°C)

Fig. 2 Thermogravimetric records of all three studied DPP derivatives Table 1 Thermogravimetric analysis of all three studied DPP derivatives Derivative Tdegradation [ C]

N,N0 -EtAd-Th-DPP 370

N,O0 -EtAd-Th-DPP 264

O,O0 -EtAd-Th-DPP n.d.

Fig. 3 Absorption and fluorescence spectra of studied DPPs diluted in chloroform

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Table 2 Optical parameters of DPPs determined from absorption and fluorescence measurements Derivative λABS max [nm] ε [dm3mol1 cm1] λABS edge [nm] λPL[nm] Stokes shift [eV] τ [ns] Eg opt [eV]

N,N0 -EtAd-Th-DPP 549 25,000  1000 572 562 0.05 6.07  0.01 2.23

N,O0 -EtAd-Th-DPP 533 12,000  200 602 598 0.26 0.65  0.02 2.16

O,O0 -EtAd-Th-DPP 505 26,000  1000 598 731 0.76 0.32  0.05 2.05

Fig. 4 Spectral response of O,O0 -EtAd-Th-DPP in acid conditions

It is known that unsubstituted trivalent nitrogen in case of O-alkylation has high affinity to hydrogen and can be reversibly protonated (i.e. halochromic effect) upon addition of acid into solution [32–34], which results in a bathochromically shifted maximum of absorption spectrum. Spectral response of acidified solution (1 vol% of glacial acetic acid 99.8%) of O,O0 -EtAd-Th-DPP derivative is shown on Fig. 4. Assuming protonation of both nitrogen atoms, the absorption maximum was measured at 565 nm and the shape of the spectrum with clean zero-phonon transition peak is similar to N,N0 -substituted derivative. Interestingly, the absorption edge of protonated and deprotonated form is practically same at about 600 nm. The position of alkyl group has also a major influence on fluorescence emission spectra (see Fig. 3). The emission maximum of N,N0 -EtAd-Th-DPP was at 562 nm and thus Stokes shift was very small only 0.05 eV (see Table 2). Emission maximum

Novel Adamantane Asymmetrically Substituted Diketopyrrolopyrroles

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of N,O0 -EtAd-Th-DPP was bathochromely shifted to 598 nm and Stokes shift increased to 0.26 eV, thus N,O0 -EtAd-Th-DPP is more reorganized after excitation than N,N0 -substituted one. Moreover, emission spectrum of N,O0 -alkylated derivative has resolved vibronic structure in contrast to absorption/excitation spectrum. It is showing that excited molecule is more planar than in ground state. Clear emission spectrum of O,O0 -EtAd-Th-DPP derivative was determined with maximum at 731 nm. The fluorescence quantum yield dramatically dropped with substitution in O-position. While N,N0 -EtAd-Th-DPP had relatively high fluorescence quantum yield about 60%, N,O0 -EtAd-Th-DPP showed only up to 1%. Fluorescence of O, O0 -EtAd-Th-DPP was found to be too small for quantitative evaluation. Fluorescence quantum yield relates to fluorescence lifetime, which was found for N,N0 -EtAd-Th-DPP about 6 ns, for N,O0 -EtAd-Th-DPP about 0.6 ns and for O, O0 -EtAd-Th-DPP only 0.3 ns. Intersection point of absorption/excitation and emission spectra provides information about optical band gap, which was found for N,N0 -EtAd-Th-DPP 2.23 eV, for N,O0 -EtAd-Th-DPP 2.16 eV and for O,O0 -EtAd-Th-DPP 2.05 eV, respectively. These values are very close and they are thus further confirm the above hypothesis about suppressed electron transitions from ground state to excited state with the lowest vibration energy in case of O-substituted derivatives.

4 Conclusion In summary, a novel series of three thiophene-DPP derivatives alkylated by ethyladamantyl chains was synthesized and subsequently investigated. It could be summarized that O-substitution is a significant competitive reaction to N-alkylation of the DPP molecule containing lactam groups and leads to the formation of considerable amount of side-products. Thermogravimetric measurements revealed that O-alkylation of the DPP results in formation of much less stable derivatives in comparison to the product of N,N0 -alkylation. The study of optical properties revealed that O-substituted derivatives tend to possess large Stokes shift which reduces the reabsorption of the emitted luminescence. Moreover, the substitution on the highly polar ketone group induces electron cloud polarization, which is beneficial for the two-photon absorption. Thus such materials are potential candidates for the application as e.g. biomarkers. However, O,O0 - and N,O0 - substituted DPP derivatives also have their drawbacks, such as lower synthesis yields or lower thermal stability. On the other hand, ethyladamantyl side chain as rigid alicyclic substituent very effectively contributed to increase of melting point and thermal stability of all derivatives studied in comparison to already described DPP derivatives alkylated with common linear or branched alkyl chains, e.g. 2-ethylhexyl [28]. These findings provide valuable information about yet overlooked regioisomers of the alkylation of DPP derivatives, in terms of both thermal stability and solution optical properties.

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Acknowledgments This work was supported by the Czech Ministry of Industry and Trade Grant TRIO FV20022 and MC thanks project No. FCH-S-19-5834.

References 1. Bagher AM (2014) Comparison of organic solar cells and inorganic solar cells. Int J Renew Sustain Energy 3(3):53–58 2. Hao Z, Iqbal A (1997) Some aspects of organic pigments. Chem Soc Rev 26(3):203–213 3. Gsänger M, Huang DBL, Stolte M, Würthner F (2016) Organic semiconductors based on dyes and color pigments. Adv Mater 28(19):3615–3645 4. Farnum DG, Mehta G, Moore GGI, Siegal FP (1974) Attempted reformatskii reaction of benzonitrile, 1,4-diketo-3,6-diphenylpyrrolo[3,4-c]pyrrole. A lactam analogue of pentalene. Tetrahedron Lett 15(29):2549–2552 5. Nielsen CB, Turbiez M, McCulloch I (2013) Recent advances in the development of semiconducting DPP-containing polymers for transistor applications. Adv Mater 25(13):1859–1880 6. Grzybowski M, Gryko DT (2015) Diketopyrrolopyrroles: synthesis, reactivity, and optical properties. Adv Opt Mater 3(3):280–320 7. Bronstein H, Chen Z, Ashraf RS et al (2011) Thieno[3,2-b]thiophene-diketo-pyrrolopyrrolecontaining polymers for high-performance organic field-effect transistors and organic photovoltaic devices. J Am Chem Soc 133(10):3272–3275 8. Chen TL, Zhang Y, Smith P, Tamayo A, Liu Y, Ma B (2011) Diketo-pyrrolopyrrole-containing oligothiophene-fullerene triads and their use in organic solar cells. ACS Appl Mater Interfaces 3 (7):2275–2280 9. Li Y, Sonar P, Singh SP, Soh MS, Van Meurs M, Tan J (2011) Annealing-free high-mobility diketopyrrolopyrrole-quaterthiophene copolymer for solution-processed organic thin film transistors. J Am Chem Soc 133(7):2198–2204 10. Yumusak C, Abbas M, Sariciftci NS (2013) Optical and electrical properties of electrochemically doped organic field effect transistors. J Lumin 134:107–112 11. Glowacki ED, Romanazzi G, Yumusak C et al (2015) Epindolidiones-versatile and stable hydrogen-bonded pigments for organic field-effect transistors and light-emitting diodes. Adv Funct Mater 25(5):776–787 12. Vala M, Weiter M, Vyňuchal J, Toman P, Luňák S (2008) Comparative studies of diphenyldiketo-pyrrolopyrrole derivatives for electroluminescence applications. J Fluoresc 18 (6):1181–1186 13. Kalyani TN, Dhoble SJ (2012) Organic light emitting diodes: energy saving lighting technology – a review. Renew Sust Energ Rev 16(5):2696–2723 14. Qu S, Wu W, Hua J, Kong C, Long Y, Tian H (2010) New diketopyrrolopyrrole (DPP) dyes for efficient dye-sensitized solar cells. J Phys Chem C 114(2):1343–1349 15. Huo L, Hou J, Chen H-Y, Zhang S, Jiang Y, Chen TL, Yang Y (2009) Bandgap and molecular level control of the low-bandgap polymers based on 3,6-dithiophen-2-yl-2,5-dihydropyrrolo [3,4-c]pyrrole-1,4-dione toward highly efficient polymer solar cells. Macromolecules 42 (17):6564–6571 16. Qu Y, Hua J, Tian H (2010) Colorimetric and ratiometric red fluorescent chemosensor for fluoride ion based on diketopyrrolopyrrole. Org Lett 12(15):3320–3323 17. Sokolov AN, Roberts ME, Bao Z (2009) Fabrication of low-cost electronic biosensors. Mater Today 12(9):12–20 18. Fischer GM, Jüngst C, Isomäki-Krondahl M, Gauss D, Möller HM, Daltrozzo E, Zumbusch A (2010) Asymmetric PPCys: strongly fluorescing NIR labels. Chem Commun 46 (29):5289–5291

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19. Bürgi L, Turbiez M, Pfeiffer R, Bienewald F, Kirner H-J, Winnewisser C (2008) High-mobility ambipolar near-infrared light-emitting polymer field-effect transistors. Adv Mater 20 (11):2217–2224 20. Zhang J, Kang D-Y, Barlow S, Marder SR (2012) Transition metal-catalyzed C–H activation as a route to structurally diverse di(arylthiophenyl)-diketopyrrolopyrroles. J Mater Chem 22 (40):21392–21394 21. Liu S-Y, Shi M-M, Huang J-C et al (2013) C–H activation: making diketopyrrolopyrrole derivatives easily accessible. J Mater Chem A 1(8):2795–2805 22. Chan WK, Chen Y, Peng Z, Yu L (1993) Rational designs of multifunctional polymers. J Am Chem Soc 115(25):11735–11743 23. Beyerlein T, Tieke B (2000) New photoluminescent conjugated polymers with 1,4-dioxo-3,6diphenylpyrrolo[3,4-c]pyrrole (DPP) and 1,4-phenylene units in the main chain. Macromol Rapid Commun 21(4):182–189 24. Iqbal A, Jost M, Kirchmayr R, Pfenniger J, Rochart A, Wallquist O (1998) Bull Soc Chim Belg 97:615 25. Tieke B, Rabindranath AR, Zhang K, Zhu Y (2010) Conjugated polymers containing diketopyrrolopyrrole units in the main chain. Beilstein J Org Chem 6:830–845 26. Naik MA, Venkatramaiah N, Kanimozhi C, Patil S (2012) Influence of side-chain on structural order and photophysical properties in thiophene based diketopyrrolopyrroles: a systematic study. J Phys Chem C 116(50):26128–26137 27. Frebort Š, Eliáš Z, Lyčka A, Luňák S, Vyňuchal J, Kubáč L, Hrdina R, Burget L (2011) O- and N-alkylated diketopyrrolopyrrole derivatives. Tetrahedron Lett 52(44):5769–5773 28. Zhao B, Sun K, Xue F, Ouyang J (2012) Isomers of dialkyl diketo-pyrrolo-pyrrole: electrondeficient units for organic semiconductors. Org Electron 13(11):2516–2524 29. Zhang L, Shen W, He R, Liu X, Tang X, Yang Y, Li M (2016) Fine structural tuning of diketopyrrolopyrrole-cored donor materials for small molecule-fullerene organic solar cells: a theoretical study. Org Electron 32:134–144 30. Kovalenko A, Yumusak C, Heinrichová P et al (2017) Adamantane substitutions: a path to high-performing, soluble, versatile and sustainable organic semiconducting materials. J Mater Chem C 5(19):4716–4723 31. Grzybowski M, Glodkowska-Mrowka E, Hugues V, Brutkowski W, Blanchard-Desce M, Gryko DT (2015) Polar diketopyrrolopyrrole-imidazolium salts as selective probes for staining mitochondria in two-photon fluorescence microscopy. Chem Eur J 21(25):9101–9110 32. Qian G, Qi J, Davey JA, Wright JS, Wang ZY (2012) Family of diazapentalene chromophores and narrow-band-gap polymers: synthesis, halochromism, halofluorism, and visible–near infrared photodetectivity. Chem Mater 24(12):2364–2372 33. Zhou N, Vegiraju S, Yu X et al (2015) Diketopyrrolopyrrole (DPP) functionalized tetrathienothiophene (TTA) small molecules for organic thin film transistors and photovoltaic cells. J Mater Chem C 3(34):8932–8941 34. Govindan V, Wu CG (2017) Facile synthesis of low band-gap DPP–EDOT containing small molecules for solar cell applications. RSC Adv 7(46):28788–28796

Machine Learning Approach on Steel Microstructure Classification Haon Park and Abdullah Öztürk

Abstract The microstructure of a material is its inner morphological features. The microstructure of steel can be diverse and complex depending on the composition, heat treatment, and processing of the alloy, making it difficult to accurately predict the material’s property and composition without physically analyzing the microstructure. Since the microstructure of steel can determine its physical and chemical properties as well as its performance, cost, and efficiency, it is crucial to accurately classify the microstructure. Although microstructure characterization is widespread and well known, it is mostly conducted manually by human experts analyzing pictures taken by either a scanning electron microscope or a light optical microscope. This research aims to automate this processing using state-of-the-art Machine Learning architectures and models to train and learn to differentiate, classify, and interpret the microstructure pictures, employing a pixel-wise segmentation method via U-NET architecture built upon FCNN, Fully Convolutional Neural Network. The method employed several techniques ranging from data augmentation, Amazon computing service, to semantic segmentation. The system achieved a maximum classification accuracy of 98.689%, and predicted the mechanical property with 10% error, providing a robust, accurate approach for the difficult task of microstructure classification. Keywords Steel microstructure · Machine learning · Steel phases

H. Park (*) Oasis International School, Ankara, Turkey A. Öztürk Middle East Technical University, Ankara, Turkey © Springer Nature Singapore Pte Ltd. 2021 J. M. Park, D. R. Whang (eds.), EKC 2019 Conference Proceedings, https://doi.org/10.1007/978-981-15-8350-6_2

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1 Introduction 1.1

Steel Microstructure and Phase Changes

Steel is still one of the most important and extensively used alloy materials because of its excellent mechanical properties while keeping costs low which gives a huge variety of applications [1]. The principal elements in most steel, in addition to iron and carbon, are boron, chromium, manganese, nickel, tungsten, and vanadium [2]. The mechanical properties of steel are mainly determined by its microstructure which are closely dependent on its chemical compositions. Traditionally, the microstructures of steel are characterized by using standard metallographic procedures based on chemical etching and optical microscopy and they are compared with reference series [3]. Moreover, the steel’s microstructural comparison with reference series is dependent on the expert’s subjective opinion. The microstructure of the steel is usually controlled by heat treatment which accompanies eutectoid transformation from austenite to pearlite. During the eutectoid phase transformation, FCC austenite is transformed into two different solid phases of BCC ferrite and cementite (Fe3C) below 727
C. Here the two phases are placed as alternative layers and it is named pearlite since its appearance is like a pearl under the microscope as seen in Fig. 1. If the microstructure consists of more than one phase, the properties of the steel strongly depend on the type and distribution of the different phases in order to access the understanding between structure and property relationship.

Fig. 1 Schematic representation of the formation of pearlite from austenite [4]

Machine Learning Approach on Steel Microstructure Classification

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Machine Learning Approach

This motivation leads to the consideration of Machine Learning methods, recently grabbing the attention of scientists due to their strong ability to learn high-level features from raw input data. Machine learning is based on artificial neural networks which mimic the neural connections and information processing understood to take place human brains. The most well-known machine learning algorithm is the convolutional neural network (CNN) for pattern recognition and image processing [5]. While the use of CNN methods made great strides for segmenting and classifying pathologies in biological and medical imaging [6], the methods are still new in their application to materials and structures analysis. In this research, Machine Learning technique was used to examine the structureproperty linkage of two-phase mixed steel sample. To distinguish the two different phase area, Scanning Electron Microscopy (SEM) images were trained and classified with U-NET (FCNN) architecture. U-NET was originally invented and first used in biomedical image segmentation [7]. U-NET architecture, named after the ‘u’; shape as seen in Fig. 2, can be separated into two parts. The first part is called down sampling where convolution blocks are applied followed by max-pool down sampling to encode the input image into feature representations at multiple different levels. The second part of the network consists of up sampling and concatenation followed by regular convolution operations [8]. The use of the U-NET architecture allows working with very few training images while yielding precise segmentation.

Fig. 2 U-NET Convoluted Neural Network Architecture [8]

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2 Experiment 2.1

Procedure

Steel samples of 2 cm diameter and 2 cm height were heat treated at 900
C for 1 h and then cooled in a salt bath to form a mixture structure of pearlite and ferrite. The samples were polished with sandpapers of #600, #1000, and #1200 successively, and with diamond suspension to create a mirror surface. Chemical etching was applied to selectively corrode microstructural features (grain boundaries, morphological features, pores, cracks, etc.) through putting a sample into a Nital solution (2 ml HNO3 + 98 ml Ethanol) for 6 s. Microhardness was tested on the steel surface with Vickers indenter and calculated by measuring the diagonal crack lengths. Scanning Electron Microscope was operated to capture images of the steel microstructures. All the images were cropped to equal pixel size and converted it to JPG. The images were separated into training data (used for training the model) and testing data (used to test the effectiveness and accuracy of the model). The training data images were manually color segmented into pearlite and ferrite. Data augmentation was performed on the training images to generate 792 images from 18. AWS EC2 p2.8large instance was set up and connected via terminal on a computer. The EC2 GPU services were connected to an agent and the images were trained on a U-NET model based on FCNN. The testing data images were tested on the new model and the results were stored. The accuracy of the model was measured and the properties of the microstructure were determined through a ratio test.

2.2

Experimental Observations and Data

Physical The surface of the prepared steel sample was carefully grinded and polished with a polisher. Polycrystalline diamond suspensions of 6- and 1-μm particle size were used to make a mirror surface. Figure 3 shows the microscope image of ferrite and pearlite phases which was taken by SEM. While the dark area is the ferrite phase, the alternating layer areas with bright strips are pearlite phase. The pearlite phases are well developed during the cooling history and grain boundaries are observed clearly because of the proper chemical etching. Figure 4 shows the optical microscope image and the indentation after the Vickers hardness test. By measuring the edge lengths of the diamond shape indentation, the microhardness was calculated with the following equation, HV ¼ 1:854 F=D2




ð1Þ

Machine Learning Approach on Steel Microstructure Classification

Fig. 3 Scanning electron microscope image of ferrite and pearlite phases

Fig. 4 Optical microscope image of ferrite and pearlite phases with the indention

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Fig. 5 Flow chart process of the training and testing of the model

where HV is the Hardness Value, F is the force (0.1 kgf), and D is the area of indentation. The calculated microhardness was 122.6  2.4 MPa. Hafiz [9] investigated the effect of matrix structure on the mechanical properties in iron and suggested that hardness and pearlite volume fraction has the following relationship, HV ¼ 0:0128P2 þ 1:865P þ 127:78

ð2Þ

where P is the volume fraction of pearlite. Therefore, the mechanical hardness of steel could be estimated if the pearlite volume fraction is provided. Computational The process for the training and testing of the images begins with the importation of the images at the left, and proceeds with DTL (data augmentation), to training on UNET, and ends with the inference of testing images as represented in Fig. 5. One of the challenges faced in using AWS Ec2 GPU, was memory as the processes were resource heavy, and the root volume was increased to 1 TB. The operating system of the AWS computers is Linux and the system was managed through a single terminal.

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Table 1 Results of U-NET architecture Type Large training Small training

Input image size (pixel) 256  256

Number of images 792

128  128

792

Epochs 12 5

Accuracy (%) 98.689

Loss (%) 0.036

98.095

0.0526

Fig. 6 The accuracy graph with epochs of large training images

Fig. 7 The loss graph with epochs of large training images

3 Results 3.1

Training Accuracy

Table 1 summarizes the comparison between large and small images analyses. The accuracy of the large training images, which were resized as 256  256 pixel and with 12 epochs, is 98,689%, while that of the small training images, which were resized as 128  128 pixel and with 5 epochs, is 98.095%. It is observed that both results convey noticeably high accuracy with the use of U-NET architecture. Figure 6 shows the accuracy graph with large training images, and Fig. 7 shows the loss graph with large training images according to epoch times (generations). Figure 8 shows the accuracy graph with small training images. Figure 9 shows the loss graph with small training images. The rapid progress the algorithm makes and learns each generation can be observed. Small training took about 25 min while large training took about 40 min.

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Fig. 8 The accuracy graph with epochs of small training images

Fig. 9 The loss graph with epochs of small training images

Fig. 10 Examples of successful cases in SEM segmentation using the U-NET architecture

3.2

Phases Differentiation

All the machine learning training and testing were carried out through Amazon Cloud Computing Service (AWS). Figure 10 shows successful examples of SEM segmentation using FCNN networks. It can be said that most of the objects in each microstructure image are classified correctly. From the data in Table 1 and the accuracy graphs, the Machine Learning algorithm equipped with U-NET architecture model based on the FCNN successfully distinguished the pearlite phases from the ferrite phases.

Machine Learning Approach on Steel Microstructure Classification

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Analysis

Machine Learning techniques were capable of separating ferrite and pearlite regions into different colors. Collecting only the pearlite region would be considered as the fraction of pearlite phase. Three SEM images were selected as seen in Fig. 11. After applying the phase classification algorithm, the amounts of pearlite phase were calculated as 14% for the first row SEM image, 23% for the second row, and 9% for the third row. Those pearlite phase fractions were inserted to Eq. (2), and then the estimated hardness was able to be acquired as 132.9, 138.8, and 130.3, respectively. Table 2 summarizes the difference between measured and estimated hardness values.

Fig. 11 SEM images for pearlite phase segmentation Table 2 Comparison of examined Vickers hardness and estimated hardness values Sample no. a b c Average

Measured hardness (HV) 122.6  2.4

Estimated hardness (HV) 132.9 136.8 130.3 133.3  3.5

Difference (%) 8.4 11.6 6.3 8.7  2.5

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The average difference between the measured and estimated hardness values is 8.7%, so it can be said that estimating the mechanical properties with machine learning image segmentation is a practical and feasible technique. Some of the ways this experiment could be improved or increased in accuracy is by having more standardized images. Some of the images were taken at slightly different magnifications, which may result in larger or smaller pearlite and ferrite ratio compared to the actual. Another straightforward method to increase the accuracy is to have more training data. Training size of perhaps 50 or 100 images will significantly improve the accuracy, but it will also increase the time it takes to create and train the ground truth and require more computing power. One of the problems that was faced during the experiment was in the manual colorization of the ground truth. As labeling each 18 training images of every pearlite and ferrite can be timeconsuming and tedious, mistakes were made such as labeling pearlite and ferrite incorrectly or going out of the boundaries of the phases. The flaws were directly used for training, and some of the mistakes may have appeared in the inferring process, thus resulting in a slight decrease in accuracy.

4 Conclusion In conclusion, this project demonstrates the feasibility of an effective steel microstructural classification using Machine Learning method to distinguish the pearlite phases from the ferrite phases. A pixel-wise microstructural segmentation using U-NET architecture proved to be competent in its performance and demonstrated its capabilities. The additional computing techniques of data augmentation and cloud computing demonstrated its power. The algorithm was able to differentiate the pearlite phases from ferrite phase with an accuracy of above 98%. On the prediction of mechanical property, the algorithm predicted a hardness of 133.3  3.5 HV, while the actual hardness of the real sample was 122.6  2.4 HV. The hypothesis of machine learning methods accurately and efficiently analyzing and differentiating the phase types and determine the percent ratio of pearlite and its properties were proven to be true. In the end, it can be concluded that Machine Learning is an effective and practical technique to classify materials’ microstructure and to predict the structure-property linkage. In order to take this research further, training with images of different types of microstructure with additional phases such as martensite and bainite will test the multi-differentiation ability of the algorithm. Also, not only differentiating the phases but the way it was treated such as tempered martensite vs not tempered martensite will be a fruitful opportunity to test the capability of machine learning applications.

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References 1. Krauss G (2005) Steel: processing, structure, and performance, 2nd edn. ASM International, Cleveland 2. Wilbraham AC, Staley DD, Matta MS, Waterman EL (2008) Prentice Hall chemistry. Pearson Prentice Hall, Needham 3. Bhadeshia HKDH (2013) Multiple, simultaneous, martensitic transformations: implications on transformation texture intensities. Mater Sci Forum 762:9–13 4. Callister WD, Rethwisch DG (2015) Materials science and engineering, 9th edn. Wiley, Hoboken 5. Wang ZL, Adachi Y (2019) Property prediction and properties-to-microstructure inverse analysis of steels by a machine–learning approach. Mater Sci Eng A 744:661–670 6. Yu KH, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719–731 7. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention-MICCAI 2015, Lecture notes in computer science 9351. Springer, Cham, pp 234–241 8. Medium Homepage. https://medium.com/@keremturgutlu/semantic-segmentation-u-net-part-1d8d6f6005066 9. Hafiz M (2001) Mechanical properties of SG-iron with different matrix structure. J Mater Sci 36 (5):1293–1300

Metabolomics and Its Applications to Personalized Medicine Lee Sherlock and K. H. Mok

Abstract The basis of this review is to evaluate the field of metabolomics. The strategies used in this field will be explored to understand the process of biomarker discovery, especially those with clinical value, giving rise to personalized medicine. Metabolomics is the process of profiling metabolites in a biological system, due to this it has significant potential in decoding the ultimate product of the genomic processes. It is becoming increasingly clear that the field has possible limitations that is resolved by a potential metabolomic assay that has the ability to directly target a selection of metabolites combating the issue of variability amongst samples and allow for reproducible data. Recently, an increased effort has been made to formulate a universally accepted approach. Enabling the field to progress into to a more clinical-based setting. Diseases in a biological system tend to have a “signature” of sorts: a fluctuating metabolite profile is a representation of cellular activity. Monitoring such fluctuations allows for health care that accounts for external factors such as (i) lifestyle, (ii) environmental factors and (iii) genetic information in advance of treatment. Keywords Metabolomics · Personalized medicine · NMR · Biomarker

1 Introduction The primary concern of this review is the role of metabolomics and clinically useful biomarkers for disease diagnoses. Displaying the variations between individuals in therapeutic outcome and disease susceptibility is a common challenge in clinical practice “due to complicated interactions between genetic and environmental factors [1]”. The concept of personalised medicine is of great interest as it is a therapeutic approach involving the use of genetic and epigenetic information to tailor drug L. Sherlock · K. H. Mok (*) Trinity Biomedical Sciences Institute (TBSI), School of Biochemistry & Immunology, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 J. M. Park, D. R. Whang (eds.), EKC 2019 Conference Proceedings, https://doi.org/10.1007/978-981-15-8350-6_3

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therapy and preventative care. This review highlights important aspects of the innovative field of metabolomics, namely, measurement methods of the metabolome, the contributions that metabolomics has made, and could potentially still make with regards to personalizing medicine. Furthermore, we also present the current advancements in statistical analysis and methodologies that are enhancing the field of metabolomics in an attempt to personalize medicine. The objective of personalized medicine is to grant health-care workers the tools to prescribe medicine tailored to the needs of the patient in an adequate time frame in an attempt to maximize efficacy and minimize side-effects. This will allow the prediction of possible diseases in the near or distant future, assessing susceptibility among populations. Metabolic variation could hold the key to personalized medicine. A variation of the genome, proteome, transcriptome and metabolome could lead to variation in therapeutic outcome or disease susceptibility of a patient, with this concept in mind it is necessary for the combination of fields such as; metabolomics, genomics and proteomics as it could yield an improved understanding of functional changes that accompany a specific disease or abnormality in the biological system. This progressive approach will be the driving force for providing personalised medicine for the population. Metabolomics is the most recent addition to the -Omics disciplines. The endeavour of this field is to record all metabolites within a biological sample, the primary ambition being to create a global understanding of said system. Metabolites are understood to be by-products of cellular metabolism with a weight of 1 kDa or less [2]. Water-soluble metabolites have the ability to communicate with the environment and the microbiome due to the mobility around the open biological system [3]. Consequently, metabolomics is essential for “systems biology” due to its particular scope analogous to field such as genomics and proteomics [4]. Hence, genomics and proteomics identify what could happen, metabolomics identifies what is currently happening in a system. “This realization demands a different perspective and requires the measurement of transcriptional, proteomic and metabolomics data in order to obtain a complete picture of the systems response to environmental or genetic stress [4]”. Various research has shown the relative downregulation and upregulation of genes or proteins in order to interpret fluctuations in a biological function. Likewise, regular metabolic pathways such as gluconeogenesis and glycolysis varies in terms of the cellular concentration of an enzyme over time, but this doesn’t automatically lead to a proportional alteration in metabolic flux [5, 6]. Metabolomics encompasses the complete analysis of low molecular weight molecules (metabolites) in biological systems and such an investigation is done, typically, through techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), in combination with multivariate statistical analysis [7]. The objective is to observe fluctuations in biomarker concentration and to identify a coherent relationship among fluctuations and specific disease states or external perturbations such as diet or therapeutic intervention. This is typically based on the knowledge that an infection is expected to alter homeostasis ultimately leading to variability in biomarker concentrations and/or profiles. Therefore, metabolomics has the potential ability to both diagnose and monitor various

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Fig. 1 A brief timeline of metabolomics development and applications. (Adapted from [8])

diseases, especially if based on samples which may be collected non-invasively, such as blood and urine. Since 2008 more than 140 papers have been published on disease research using biofluids metabolomics (biofluids, mainly; blood plasma, serum, urine and other biofluids more specific of the conditions under study) [7] (See Fig. 1 for a brief timeline). In 2012, it is apparent that productivity was exponential by comparison, awareness of metabolomics and enticing new strategies which can advance our understanding of diseases and management to the scientific and medical communities.

2 Technologies Used in Metabolomics Metabolomics has a wide variety of tools to use during its some of which include infrared spectroscopy, Raman spectroscopy and capillary electrophoresis, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), all used in metabolomics investigations with distinct advantages and disadvantages [9]. Biomolecular NMR is the primary tool for the investigation of protein structure and protein interactions. The understanding of a protein crystal structure is vital in several fields of research such as; structure-based drug design, homology modelling and functional genomics [10]. NMR is a quantitative method that possesses a simple sample preparation with the ability to measure multiple analytes over a broad range of conditions [11]. However, NMR has one distinct disadvantage, low sensitivity, but this can complement a variety of other tools like liquid chromatography (LC) or ultra-high performance liquid chromatography (UHPLC) to increase the resolution and sensitivity for highthroughput profiling [12]. This information can in turn be generated and compared in

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relation to the identification of metabolites in complex mixtures [13]. For example, NMR used in conjunction with UHPLC was developed in order to identify metabolic fluctuations in esophageal cancer patients to identify possible biomarkers for early diagnosis and prognosis [14]. The study revealed considerable alterations in ketogenesis, glycolysis and tricarboxylic acid cycle and amino acid and lipid metabolism in esophageal cancer patients compared with the controls [12]. In contrast, the highly sensitive MS allows for detection of subtle metabolic alterations that otherwise is undetectable via NMR. Increasing the sensitivity also increases the number of peaks detected, which is common, untargeted MS metabolomics studies are usually not quantitative in nature [15]. MS is at a distinct disadvantage to detecting metabolites that are not readily ionized, and contaminants within the sample can also change the ionization efficiency of metabolites [16]. A relatively narrow nominal mass and mass defect distribution of the metabolome which is an alternate issue in MS leading to significant peak overlap [17]. The resolution to such disadvantages is to use MS in conjunction with LC or gas chromatography (GC). Growing trends in metabolomics have studies perform analyses on the same sample in tandem using both NMR and MS [18]. In such cases, NMR can identify trends in metabolic alterations along main metabolic pathways and provides a context that aids interpretation of MS and the low abundance metabolites identified. This combining of NMR and MS methods allow for the simultaneous analysis of two large and diverse data sets [4].

3 NMR Metabolomics: Cutting Edge Advances The primary interest of NMR metabolomics studies is achieving high confidence in metabolite identification. Previous NMR studies conducted on four distinct human populations described variability among metabolite concentrations in bio-fluids of 4630 patients. The observable properties of an organisms metabolome was a consequence of factors that include; diet, genetics, environmental and microbial activity. From this, it was easy to discriminate particular metabolites to be associated with blood pressure in urine, this held true across all four populations [12]. As of late, dynamic nuclear polarization (DNP) has evolved from its days as a structural biology tool. Currently it holds tremendous potential in the field of solution-state metabolomics [19]. A sample that has been frozen at roughly 1.5 K, from this a temporary hyperpolarization is induced by polarizing the frozen sample in the presence of microwave free radicals. The hyperpolarization effects spin active nuclei by the mechanism of polarization transfer from electrons to nuclei [4]. The sample requires rapid melting and transfer to an NMR spectrometer, done in this manner due to the greatly enhanced sensitivity (>10,000-fold) [20]. The increasing sensitivity negates the requirement of radioactive labelling, allowing the detection of low-abundance metabolites [4]. DNP experiments are limited by the redistributing of the populations of nuclear spin states in order to reach the thermal equilibrium distribution, known as T1

Fig. 2 Clinically useful biomarkers. (Originally sourced from the Mayo Clinic, US [24]. U urine, P plasma, Sm saliva, Sa saliva, Se semen, WB whole blood, BS dried blood spot, C CSF (cerebrospinal fluid))

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relaxation rates, giving rise to a small measurement window of the polarized sample [21]. DNP also requires substantial hardware modifications such as a microwave generator in order to thaw and shuttle the sample to the NMR spectrometer. DNP has also been applied to carbon 13-labelled metabolites that then are used as an indicator compound for imaging [22]. Close proximity is required for the polarizer and magnetic resonance imaging spectrometer to allow for rapid transfer, dissolution, and injection of the Carbon 13-labeled metabolite given the relatively short T 1 of 30–40 s for a Carbon 13-labeled carboxyl group [4]. DNP has been previously used to monitor a single metabolite such as pyruvate in living tissue [23]. The challenges of DNP are the limitation by the number of metabolites, carbon 13-labeled, that can be polarized at the concentrations required for imaging, usually ranging from 25 to 80 mM. Furthermore, Urea, pyruvate, bicarbonate, glutamine, fumarate and dehydroascorbate have all been used for imaging [22]. Due to the issues in reproducing DNP, protocols and technology are rapidly advancing to 1 day become a regular tool of metabolomics studies [4].

4 Data Handling As previously mentioned, metabolomics studies consistently use a combination of both NMR and MS, this in turn leads to the generation of large data sets (Fig. 2). The process of “data handling” has now become quite a difficult task as there does not exist an analytical software that can interpret the data sets from both methodologies. Both NMR and MS require various “pre-data handling” mechanisms and algorithms before proceeding, the process applied to both techniques are not too dissimilar from one another. The data obtained from NMR must undergo Fourier transformation and then phased whilst the data set obtained from MS requires centroiding and de-isotoping [4]. Previously the software developed tended to be restrictive to solely one method. On the contrary the input in order to advance the technology to the point that it would be capable of working with NMR and MS data sets has been minimalistic [25]. In order to incorporate the large data sets obtained by NMR and MS into a singular comprehensible study, there are two generalised approaches. The first of the two approaches analyzes the data set independently, comparing and contrasting to observe metabolite fluctuations. Simplicity makes this approach particularly advantageous as it doesn’t require significant adjustments to the protocol, and the confidence level is also increased if the presence of a metabolites concentration in a biological system can be detected by both methods [4]. It must be noted that this method can be a leading cause of information loss due to its ambiguity in relation to the alignment of the peaks and out-sourcing information from other methodologies is often required for a resolution. In addition, the independent nature of the methods mean a demanding task to establish statistical correlation. The second approach to combining NMR and MS data sets is to simultaneously integrate each data set into a single statistical model using a multi-block analysis

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[4]. Multi-block analysis is a consolidation of a variety of methods that combine multiple data sets prior to typical multivariate analysis. In addition to combining multiple instrumental data sources [26], multi-block analysis has been successfully employed to combine data sets from different “omics” disciplines [27]. For obvious reasons, multi-block methods are more desirable in contrast to independent analysis, the larger combined data set could lead to a procedure allowing research that can simultaneously resolve current problems and predict solutions to future problems that may arise [9]. As of yet, multi-block analysis is not always progressive, sometimes relying on “pre-data handling” processes using multiple software packages [25]. Once a metabolomics study has been completed additional data processes such as “Chemo-metric-based analyses” would improve the confidence level of the obtained data.

5 Metabolomics: Current Outlook As metabolomics is growing in popularity, as a discipline it must look to the future and begin to anticipate how the field may develop in order to transition into the modern-day world. Work is ongoing to improve technology and computational methods which will be discussed below. The field has matured in terms of the literature available, but Broadhurst et al. has suggested that many studies “lack statistical robustness and validity [28]”. This is not unique to the field of metabolomics as demonstrated by George Poste [29]. This article highlights that whilst many biomarkers have been described in the academic literature, very few (almost zero) have made it into the clinic. For example, while dipstick tests are currently in use for rapidly quantitating glucose levels such as in the case diabetes, this method lacks real-time prolonged monitoring the health of a patient [24]. Metabolomics should aim beyond its current status of disease monitoring and diagnosis with the ultimate goal of the field being the “prediction” of near or future health statuses by considering a baseline metabolite concentration and working in conjunction with genomics and proteomics. This approach would eradicate trial and error regimes, thereby lowering clinical mortality rates by approximately 70(%) [30]. It should be noted that patient availability can be quite diverse depending on the disease in question, and obtaining samples that are of value can prove an even more demanding task. Trivedi et al. has shown that over the past 100 years more than 1600 publications “claimed” to discover a new biomarker applying the metabolomics technique [24]. The majority of the publications lack statistical power, the sole problem being finite sample size, and in turn the biomarkers lack applicability. It is our opinion that this is one of the fundamental barriers that is hindering the transition of metabolomics into a clinical setting. This statement is appropriate when applied to the top five causes of death in the United Kingdom (heart disease, dementia/Alzheimer’s disease, malignant neo-plasms of the trachea, lung cancer and chronic lower respiratory disease) as there is a lack to identify biomarkers associated with such diseases [24, 31] (Fig. 2). For proof of principle, the top three causes of

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death across Europe are malignant neo-plasms of the trachea, chronic lower respiratory disease and heart disease [32], and recruiting candidates into a study should not be too impossible due to the abundance of patients, suggesting that the statement by Broadhurst et al. is valid [28].

6 Personalized Medicine and Disease Profiling Metabolomics can be useful in profiling the response to medical intervention by monitoring fluctuations in metabolites from fluids such as blood, sweat and urine. Metabolite profiling is quite advantageous as it al-lows for the observation of multiple metabolites at once, after further examination the field could directly correlate fluctuations to a specific disease and response to therapeutic intervention. Profiling numerous metabolites rather than a single biomarker is likely to yield a higher sensitivity and selectivity. A prime example is the plasma baseline, the levels of xanthine, 2-hydroxyvaleric acid, succinic acid, stearic acid and fructose prior to simvastatin treatment was observed to reliably predict the response mechanism in reducing lipoprotein cholesterol [33]. Models such as “optimized potential for liquid simulations” (OPLS) have a significantly high yield of 70(%) and 78(%) specificity [4]. A recent study analysed urine samples obtained from patients with Tuberculosis (TB) that took place over a 6-month period. It was found that over time, the samples obtained from both the control (non-TB patients) and the group in question had increasing similarities whilst the anti-TB therapy progressed (drugs used; isoniazid, ethambutol, or pyrazinamide) [4]. A success story of metabolomics is its use to identify metabolic fluctuations associated with psoriasis [34]. It was interesting to see the authors identified an increased demand for glutamine in association with psoriasis, this biomarker had previously not be identified as an indicator [35]. Glutamine in excess demand has also been associated with diseases characterized by increased cellular proliferation, such as in cancers. The same metabolomics study also identified β-sisosterol which is a commonly employed herbal remedy, therefore suggesting that metabolomics may also be used for the identification of external treatments outside of the knowledge or recommendation of the physician. “More importantly, the metabolomics results were consistent with trends previously observed in genomics and proteomics studies [35]” (Fig. 2). In this manner, metabolomics may assist in determining whether co-administration of a complementary treatment was beneficial or detrimental to a therapeutic outcome of the patient [4]. Furthermore, metabolomics will have a significant impact in decreasing patient mortality rates, this is due to its ability of disease prognosis and predicting the response of an individual to a drug.

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7 “Network Medicine” and Personalized Medicine Previous studies have highlighted that a disease is rarely the repercussion of a single genes irregularity but rather a representation of various stresses placed upon intracellular networks [36]. This understanding requires an advancement to encompass the complicated brain circuitry that undergoes modification with the disease. Network medicine allows for the exploration of the molecular complexity of a particular disease, this in turn leads to the identification of critical points between disease pathways, but also the molecular relationships between distinct phenotypes, a direct analogue of the patterns of the different symptoms in psychiatric disorders [37]. Barabsi et al. has also emphasized that genes associate with diseases were characterized by associating a horde of genes to a particular phenotype, in contrast to present-day, Gene Wide Association Study (GWAS) is used to characterize the single nucleotide polymorphisms that are statistically associated with the disease [36]. As of late a series of sophisticated network-based methodologies have been developed in order to predict disease associated genes [36]. Including the following methods: (i) Linkage: infers that the interactions of the disease proteins are the candidates associated with the same disease phenotype. (ii) Pathway-based: is dependent on the cellular components that apply to the same disease component, and have a high probability of being involved in the same disease. (iii) Diffusion-based: aims to identify the pathways that are closest to the known disease genes. This approach was developed in order to counter the difficulties that occur from working with single-target genes and by comparison, one-target drugs. This alternative may correct dysfunctional aspects of complex diseases, as a bi-product it may also alter the activity of other related net-works leading to undesirable side effects. This emphasizes the demand for network analysis as it is essential component of drug development. Technologies such as electro-physiology and NMR working in conjunction with pharmacogenomics and metabolomics is enabling researchers to identify networks with promise of transforming the current understanding of the mechanisms of psychiatric disorders [38]. The new information obtained from this approach holds promise in generating advances in personalized medicine [39] in psychiatry.

8 Metabolomics for the Masses Prior to the twenty-first century it would be inconceivable to think scientists could obtain large amounts of data from across the globe outside of a laboratory setting, with current technological advancements this is now feasible and being done daily

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by multiple national corporations. Hardware such as pedometers, smart watches and health apps on smart phones make this type of approach a reality [38]. This technology could obtain data on a personalised level to aid clinical studies, it is probable that studies involving exercise and diet could benefit from such an advancement. One question on everyone’s mind would be is whether it is physically plausible to give metabolomics to the masses. MS linked with LC is a powerful tool in metabolomics and biomarker discovery (Fig. 2) but has its drawbacks - it can be strenuous and expensive, making it unlikely to be a worthy candidate for large scale screening. One must take into consideration the population of the earth (estimated at >7.72 billion individuals; [40]). Personalized medicine may not be able to access its full potential until biomarkers provide enough insight to transition them successfully into wearable technology that is readily available to the wider population. Implanting biosensors into such technologies such as smart phones, smart-watches for monitoring heart conditions, necklaces and glucose monitoring contact lenses are all good innovations with the ability to make biomarker discovery a more personalised task. Technology involved in translating biochemical alterations into data and signals is not a new process, as seen in previous sections. The share quantity of biochemical reactions occur-ring in a system at any given time means that metabolomics rarely lack a sufficient quantity of data, it is often the case that a sizable population is lacking. For this reason alone the ability to translate metabolic profiles onto wearable technologies would provide addition information to the research that acts as a compliment to their own [24]. Biosensors being integrated with wearable technology in an attempt to personalizing metabolomics is a colossal task and would require immense computing power and data storage which demands an advanced cloud- computing environment [41]. Wearable technology would give researches the edge as they could be uploaded onto intelligent cloud services such as Microsoft Azure or Google Cloud [42]. The data collection of an individual over time via portable devices has the potential of producing copious amounts of data which could be interpreted via the cloud in turn producing predictions for future health risks. In the case of mobile Parkinson’s disease (PD) study that attempts to research the occurrence, presentation and management of PD symptoms via survey telemetry data using a smart-phone app [42]. A smart-phone based app can be used to monitor and further understand the association between pain and the weather conditions for people suffering from rheumatoid arthritis [34]. Innovations such as these may sound inaccessible, however, the counterargument to this is the moon-shot project by Alphabet. An ambitious research project that is attempting to advance biosensors in wearable technology and enhanced detection of cancer by understanding the inherent variability of the metabolome [43]. Although it is underdeveloped it yet has extraordinary potential in detecting early onset of diseases by examining bio-fluids. The use of artificial intelligence (AI) is now common practice for decision making. Long et al. has suggested that this method could be adjusted for the purpose of risk assessment and choice of healthcare options [44]. A real-world application of this process can be found in the United States where the FDA have authorized an AI in a clinical setting. The primary roles of the AI in such a setting is to; assess the

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health of the heart, correctly measure the volume of the ventricles and report back to the specialist, therefore, increasing the rate and safety of heart surgery. Metabolomics will soon become common practice when tackling issues such as: diagnosis, analysis, monitoring and progression of various disease. In order for this to become a reality we must first enhance our understanding of pathogen-host mechanisms and how they can impact on the chemical profile of bio-fluids [4] i.e. metabolites. Assuming the technology required for this process becomes a conventional process and affordable [30], the metabolic profiling machinery could be operated at a local GPs office. The rationale behind this would be to allow metabolite screening for a patient at regular intervals. This successfully allows for efficient disease diagnosis and monitoring, therefore improving overall health status.

9 Challenges in Metabolomics In order for metabolomics to be a complete success, biomarkers would need to transition into a clinical setting, and a globally accepted method to investigate metabolic profiles and harmonization of protocols will be a necessity [24]. Furthermore, in the case of an interchangeable method being agreed upon, the inherent differences between the labs will contribute significantly to lowering the confidence level of a potential biomarker. Clearly the lack of suitable biomarkers currently holds back the wider implementation of personalised medicine [45]. If only the one platform is used, the field couldn’t have a high confidence in the profiling of the entire metabolome, it is accepted in the metabolomics community that there is no magic tricorder that measures everything [46], implying that the community will always expect a better biomarker [24] exists. Establishing correlations between diseases and biomarker fluctuations gives physicians the ability to diagnose diseases and tailor treatments to individuals. This would radically improve circumstances [47]. Biomarker research and predicting disease-associated molecular changes in body tissues and fluids hasn’t delivered on its promise as of yet. More than 150,000 papers documenting thousands of claimed biomarkers, but fewer than 100 have been validated for routine clinical practice [29]. The issue of standardization also applies to the handling and the storage of the samples, this process can definitively affect the detection of biomarkers. A survey conducted in 2009 by the US National Institutes of Health (NIH) showed that researchers from 80 (%) of 700+ laboratories said the process of securing and standardizing samples for biomarker research was immensely difficult [12]. It is alarming to find that a similar percentage didn’t question how the handling process and sample quality may impact the outcome of the study. This track record is representative of the inability to embrace a universally accepted coordinated systems-based approach. The process of gathering candidates to participate in biomarker studies and subjecting them to a large-scale study is the cause of substantial logistical and regulatory challenges. Change is required in order to standardize the methodology and obtain the large sample sizes required for validation trials. The traditional model

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of investigator-initiated research must be replaced with the collaborative approaches typical of big-science projects such as The Cancer Genome Atlas initiative of the NIH to catalogue the genomic changes found in cancers [29]. The advancements required for biomarkers into expensive research demand industrial partners as they can contribute financially and share their expertise in: clinical-trial design, assay production, data analysis and regulatory compliance. The post marketing of genetic tests for disease risk allowed for the rise of multiple hurdles that confront biotechnology companies, such hurdles are the outcome of not standardizing tests [48]. The validation will require the involvement of specialists in healthcare economics. The healthcare environment is heavily dependent on finances, the process adopting biomarkers into the clinic needs to be cost efficient. The research must provide a proposal that is efficient enough to reduce cost and improve clinical practice, this can be done by: improving the health of the patient or eliminate expensive treatments [29]. Sakar et al. has suggested biomarker research could potentially be a factor in large research networks, industries that are involved in genetics, molecular biology, statistics, analytical-chemistry etc. would need to share their expertise for this to occur. The metabolite profiling have been thought to allow for an increase in the quality of patient care with the addition of lowering medical costs. Estimations have been made by The American Society of Clinical Oncology emphasizing that routine testing of individuals with a mutation in the K-RAS oncogene, the cause of colon cancer, would save at least US 600 million US dollars annum [29]. “This would free patients of futile and potentially toxic treatments, for example people with a mutation of this nature do not respond to drugs that inhibit epidermal growth factor receptors, this runs up a bill over 100,000 US dollars per treatment [29]”.

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The Future of Healthcare and Personalized Medicine

“The main use of metabolomics is for biomarker discovery [49]”, therefore, the detection of biomarkers in a large population and convert the data into cheap, quick, reliable methods that allow public access, making it “personal”. Personalised medicine has been practised within an evidence-based framework, in this method an individual is treated for diseases based on the most popular medicine. Post drug consumption assessments are made in order to evaluate whether this has relieved symptoms (this may involve the measurements of biomarkers that are clinically useful). Post assessment of the patient, they may; stay on the drug, be prescribed an alternate medicine or be given treatment to relieve side effects of the first drug. This is clearly a slow process and is possibly dangerous to the patient. Precision medicine can aid in combating the increased mortality globally caused by microorganisms, the most noticeable risks are HIV and Mycobacterium tuberculosis. In the previous decade antimicrobial resistance (AMR) has be-come more of a concern as previously harmless pathogens have become cause for further investigation. Neill et al. has emphasized that this growing threat that is bacterial infection will kill more humans

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than heart disease by the year 2050 [32]. Due to the ability of metabolomics to detect metabolite fluctuations when a pathogen is present, it is likely that the field will contribute to understanding of AMR and host-pathogen interaction [24]. Tasks such as these could be catapulted into success with the launch of “precision medicine initiative” which was announced during the State of the Union address by President Obama. This initiative was considered a bold new research direction [50] especially considering that accessibility of biobanks has increased which is support by the NIH [51]. Personalized medicine must take into consideration both the genotype and phenotype of the individual before they undergo medical treatment, thus, becoming dependent on analytical methods in order to assess risk and to present healthcare options [24], which is not too dissimilar from the AI approved by the FDA. This system will be dependent on a fundamental understanding of biomarkers, if the foundation is not solid then researchers would struggle to accurately identify underlying pathology, making precision medicine impossible [52]. It is com-mon knowledge that diseases is the cause of alterations in human metabolism. Therefore, the driving force behind metabolomics is it usage of biomarker discovery to enhanced portrayal of a disease phenotype, furthermore, allowing for progressive methods that ultimately grant a “cure” [53]. It is important that a metabolic profile is screened prior to a pathogen being present in the bio-logical system. Dhanasekaran et al. highlights the importance of such a procedure by using the example of prostate specific antigen (PSA) biomarker. An increased concentration of the PSA doesn’t necessarily correlate to prostate cancer but a gradually increasing PSA level occurring in con-junction with age is a characteristic of an enlarged prostate [54]. A metabolic profiling scheme that is designed well and reasonably priced may not be possible or even accessible due to multiple challenges, some of which include; labour and consumer costs, ethical, legal and social issues [24]. In order for personalised screenings to be successful the risk-benefit ratio needs to be de-fined clearly per disease [55]. The discovery of biomarkers is encouraged as research displays its potential in dramatically reducing fatal health conditions such as: cancer, congenital disease, heart and respiratory diseases. The main focus of such research being; the serum metabolome for its significance in breast cancer detection [56] and the urinary metabolome due to its significance in pathogen and kidney cancer detection [57].

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Conclusion

This review has highlighted the most recent improvements in the field of metabolomics emphasizing the benefits it could potentially have in areas such as personalised medicine and general research. The process of metabolic profiling has proven itself frequently with regard to drug and surgical intervention, decreasing risk and increasing efficiency [4].

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Many studies have been published and awarded public investment but the field requires reorganization. The progression of metabolomics is still hindered by the constitutional limitations in experimental design, the anomalous technology expense, variability amongst samples and complex data analysis [29]. A metabolomics assay that had the ability to directly target a selection of metabolites would aid with the problem of variability amongst samples and allow for reproducible data [4]. The problems that burden metabolomics and biomarker discovery reside as much in the culture and organization of academic research as in deficiencies in analytic technology [29], this review would be interested in studies that concentrate on public perception of this cloud data base. Security and marketing of this approach would need to be prioritised as a misunderstanding or the fear of hacking the database may cause for a restless public that could delay progress [58]. Science and many of its key thinkers have been subjected to this type of public scrutiny before, Darwin being a primary example. As previously validated by Broadhurst et al. [28], “the field lacks statistical robustness”, to compensate for such adversity it is important that metabolomics adopts the approach taken by the GWAS which would allow for the involvement of multiple institutions and investors. In other words, the field must drive forward towards undertaking a large cohort multi-centre studies to enhance the discovery process and obviously market the research in a manner that is well understood by the public to not hinder the progression of the field. The shift towards testing for multiple biomarkers has accelerated technological innovation for parallel automatic gene profiling, proteins, RNA molecules and metabolites. Advances like this are be little use unless amendments are made to both the organization and funding that persuade the research community to adopt common standards and a universally accepted systems-based approach to biomarkers. Several initiatives are attempting to improve the practice of tissue storage or bio-banking [59]. Funding agencies such as the NIH and the European Framework Programme support research programmes that allow access to a sufficient number of rigorously characterized samples that have established quality control involved in samples collection, handling and storage. They possess the full spectrum of cross-disciplinary capabilities needed to translate laboratory findings to the clinic. This would encourage academic laboratories to become part of larger research networks that include clinical and industrial partners. Large sample sizes required to validate studies are already generating enormous data sets and as sequencing of the entire genome becomes mainstream the data sets will only grow in size [29]. The analysis of the data sets requires sophisticated mathematical, statistical and computational skill. Altering the formats used in academia compared to industry and regulators is required to make them more compatible with the software used to produce electronic patient health records. Clinical and industrial partners can be a powerful force in driving such standardization. An example would be the established guidelines of the US National Cancer Institutes Cancer Human Biobank (caHUB) to provide healthy samples from healthy individuals. Cancer samples are collected, annotated, stored and analysed under the conditions set by caHUB, they are also accompanied by appropriate donor medical information. The creation of

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international biobanks are required. This would demand coordinated rules to allow researchers globally to access the resources and not be hindered by barriers. Recent NASA studies have given a glimpse of how powerful and useful it is to have an understanding of the human metabolome [60]. Although there is a lot of ground to cover in this field, as highlighted above, the applications of metabolomics in preventative medicine in conjunction with screening leads to limitless opportunities. Indeed, one may even say that the field of metabolomics has the potential serve an integral and essential role in the survival of humanity.

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Partition-Based Task Mapping for Communication Energy Minimization in 3D Network-on-Chip Sanghoon Kwak

Abstract Network-on-Chip designs have experienced a rapid development during the last decade and is a becoming a de-facto technique for communication among cores. 3D integration using Through-Silicon-Via (TSV) has received much attention recently as an alternative solution to overcome the bottleneck of wiring. Integrating 3D TSVs with the conventional 2D NoC takes full advantage of the characteristics TSV such as lower power consumption and much smaller delay. In this paper, an efficient task mapping algorithm to target reducing communication energy of 3D NoC, in which the procedure is decomposed into balanced min K-way partitioning algorithm and a heuristic partition arrangement to 2D plane. Experimental results demonstrate the proposed algorithm consumes much less communication energy than the previous approaches. Keywords Network-on-Chip · TSV · Task mapping · Low-energy

1 Introduction Due to saturation of technology scaling, multi-processor system-on-chip is considered as a solution to jump up performance bottleneck of a digital system with uni-core. The system with hundreds of cores has tremendous complexity in its design-phase. New paradigm is required to tackle the problems such as performance bottleneck, high energy consumption, thermal dissipation, huge design complexity, etc. Network-on-chip (NoC) is expected to be a solution to handle the above mentioned problems [1]. Especially, due to its scalability and regularity, and lower design complexity, mesh-style regular NoC has big attraction in complex multi-core system. In other side, 3D integrated circuit (IC) has been getting attention because of its advantages in performance and power consumption. By stacking convention 2D

S. Kwak (*) Intel Deutschland GmbH, Neubiberg, Germany e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 J. M. Park, D. R. Whang (eds.), EKC 2019 Conference Proceedings, https://doi.org/10.1007/978-981-15-8350-6_4

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dies with Through-Silicon-Via (TSV) and linking them with TSVs, 3D IC accomplished its superiority in performance and energy consumption [2]. In 3D IC, inter-layer data transfer is performed by TSVs. The power and delay of TSVs are considerably small compared to those of conventional 2D ICs. Thus, lots of researchers are studying 3D ICs to exploit the physical characteristics of them [2]. In this paper, we propose a new task mapping algorithm to reduce communication energy in 3D IC based regular NoCs. To exploit the energy saving of TSV maximally, we try to distribute the tasks, by which the total volume of traffic crossing through layers of 3D ICs will be maximized. However, the problem which maximize communication energy crossing layers of 3D ICs is NP-Hard problem with time complexity of O(N!). N is the number of tasks in communication task graphs. In our approach, we decompose the problems into two steps. First, the tasks in the same X, Y coordinates in 2D plane are grouped into a partition and then communication volume between these groups is minimized. In this step, we solve “Min Kway partitioning problem” by applying the heuristic algorithm which is proposed in [3]. In the second step, we arrange each partitioning of task graphs to 2D plane in the way of minimizing the aggregated traffic flowing through horizontal wire. This paper is organized as follows. In Sect. 2, the related research work will be enumerated. The motivation of the work and the architecture model will be presented in Sect. 3, and some mathematical formulation of task graph and architecture will be given. The experimental results comparing the energy consumption by the proposed method to that by random mapping, will be presented. Finally, we conclude this paper in Sect. 4.

2 Related Works Large amount of works have been presented for task mapping of NoC since the early of 2000s [1, 4]. solved task mapping problem in 2D NoC in which they investigated the effects of routing scheme and took advantage of the ‘better’ routing algorithm for communication energy minimization in 2D NoC. With related to 3D NoC architecture, various issues of 3D NoC such as performance, power consumption, and topology synthesis have been covered in [5– 10]. Among them, [5, 10] explored the design space of 3D NoC architecture, in which power consumption depending on NoC topology and task mapping is varying. They assumed an application specific topology (in other words, irregular topology) for 3D NoC architecture. To our best knowledge, there have been no works to map application tasks to tiles in 3D IC based NoC, targeting minimization of communication energy. [11–15] suggested task mapping algorithms in 3D NoCs. However, these researches used mostly genetic or evolutionary algorithms to optimize communication or computational energy. Though [12] introduces a heuristic approach to reduce total communication energy, their architectural assumption for 3D NoC is different from ours.

Partition-Based Task Mapping for Communication Energy Minimization in 3D. . .

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3 3D NOC Architecture Model for the Proposed Algorithm 3.1

Architecture Model for the Proposed 3D IC Based NoC

In 3D NoC architecture, a tile takes a role of place-holder for IPs or processing elements in the architecture. In each tile, 6-directional horizontal links are connected to a crossbar switch, and one local link is connected to a local IP. The structure of router is shown in Fig. 1. A vertical link connecting two router upward or downward is implemented by TSV of 3D IC. We assume the regular mesh-style 3D IC based NoC architecture for our work. The regular mesh-style NoC architecture has following advantages compared to its irregular counterpart. • Scalability: The regular mesh style is highly scalable in nature. Modifying a NoC architecture by adding or removing some tiles to/from the architecture is very easy. • Lower Design Complexity: Due to its scalability and regularity, the time-tomarket is more easily guaranteed. This will be especially helpful when future design trends incorporating more than hundreds of cores and IPs into one die will become true. • Predictability: Though estimating performance in measurement of time unit is known to be hard since the delay of NoC architecture dominantly depends on dynamic behavior of the routers and the input traffic [4], the estimation of energy is somewhat easier than irregular application specific NoC architecture.

Fig. 1 Structure of the router for proposed 3D IC based NoC

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3.2

S. Kwak

Motivation of the Work

In previous subsection, we explained the advantages of regular NoC architecture. In other hands, 3D IC is expected to solve the problems such as performance saturation and high-power consumption due to increased integration degree [5]. TSVs of 3D IC, which has the length of order of μm, can replace global long wires of 2D IC with the length of mm order. The capacitance for 3D TSV and 2D long wire according to dimensions (length for the 2D long wire and diameter for the cylindrical TSV wire) are given in Table 1. Also dynamic power consumption of wire Pdynamic for the corresponding dimension is presented. Cwire and Cbuffer denotes the load capacitance of wire and buffer, respectively. Generally, 2 mm is the boundary length to transfer signal without buffering. Please note that Cbuffer is even larger than Cwire in 2D long wire when l ¼ 2 mm. However, since the dimension of length in 3D TSV wire is 25 ~ 50 μm, a buffer is not required for TSV wire. This indicates that, if a 2D global long with its length over 2 mm, is replaced by 3D TSV wire, at least, around 8 times of link power reduction can be acquired. In the view of system-level power reduction, this is an important clue to reduce the whole communication energy of NoC architecture dramatically.

4 Problem Formulation 4.1

Bit Energy Model

In our approach, to characterize communication energy consumption in the proposed architecture the well-known bit-energy model is used [4]. Ebit is the energy consumed to send a bit through router. Small modification to the bit energy model was made to differentiate the energy consumed in TSVs from the energy consumed in horizontal link. Energy consumed in sending a bit from a router through horizontal link and vertical link is represented as Ebit-h and Ebit-v, respectively. Like most other tasking mapping algorithm for NoC, we used communication task graph model [4].

Table 1 Load capacitance and dynamic power consumption of 2D and 3D wire

Ebit h ¼ ERbit þ EBbit þ EHLbit

ð1Þ

Ebit v ¼ ERbit þ EBbit þ EVLbit

ð2Þ

2D wire 3D wire

l ¼ 1 mm l ¼ 2 mm d ¼ 2 μm d ¼ 5 μm

Cwire(fF) 138 345 21.7 37.2

Cbuffer(fF) 0 514 0 0

Pdynamic(μW) 1380 6877 174 298

Partition-Based Task Mapping for Communication Energy Minimization in 3D. . .

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Here, ERbit , EBbit , EHLbit , and EVLbit represents the energy consumed at router of NoC, a buffer, a horizontal link, and a vertical link (TSV), respectively. The total energy consumed to send a bit from a tile i to another tile j is represented as follows: Ei,j bit ¼ nðERbit þ EBbit Þ þ iEHLbit þ jEVLbit

ð3Þ

where, ð n  1Þ ¼ i þ j where n means the number of router which a bit traverse through starting from tile i to j. i represents the number of horizontal links passing through on its way, and the number of vertical links passing through on its way, respectively.

4.2

Architecture Model and Task Graph Model

Definition 1 [Communication Task Graph] G ¼ G(N, E) represents a directed graph in which each vertex ni is a task and each edge ei,j means a communication between vertex ni and nj. Each edge ei,j has the weight w(ei,j) which denotes the amount of communication volume from task ni to nj. Definition 2 [NoC Architecture Graph] G ¼ G(T, R) represents a directed graph. A vertex ti is a tile in 3D NoC, and ri,j means a routing path from ti to tj. For a pair of (i, j) there is a single routing path since we assume to use simple XYZ routing algorithm for our method and it is free from deadlock and simple [4]. lti ,t j denotes communication link whether it is horizontal or vertical. W(lti ,t j ) means the amount of data transferred from ti to tj. A mapping function M:N ! T returns a location of a task ni as a form of tile ti. Our objective to find mapping with minimum communication energy is defined as follows: [Communication Energy Minimization Problem in 3D NoC] Given G ¼ (N, E) and G ¼ (T, R) Find the mapping function M

Min

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