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Nano Research 2021, 14(12): 4610-4615 https://doi.org/10.1007/s12274-021-3387-y
Research Article | Issue | Published: 18 March 2021

High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes

Show Author's Information Zhong-Hai Ji1,2,  Lili Zhang1,  Dai-Ming Tang3( ),  Chien-Ming Chen4,  Torbjörn E. M. Nordling4,5,  Zheng-De Zhang6,  Cui-Lan Ren6,  Bo Da7,  Xin Li1,2,  Shu-Yu Guo1,  Chang Liu1( ),  Hui-Ming Cheng1,8
Shenyang National Laboratory for Materials Science Institute of Metal Research (IMR) Chinese Academy of SciencesShenyang 110016 China
School of Materials Science and Engineering University of Science and Technology of ChinaHefei 230026 China
International Center for Materials Nanoarchitectonics (MANA) National Institute for Materials Science (NIMS)1-1 Namiki, Tsukuba, Ibaraki 305-0044 Japan
Department of Mechanical Engineering "National Cheng Kung University", No. 1, University RoadTaiwan China
Department of Applied Physics and Electronics Umeå University 90187 Umeå, Sweden
Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800 China
Research and Services Division of Materials Data and Integrated System National Institute for Materials Science (NIMS)Ibaraki 305-0047 Japan
Tsinghua-Berkeley Shenzhen Institute (TBSI) Tsinghua UniversityShenzhen 518055 China
Keywords:
machine learning, optimization, chemical vapor deposition, single-wall carbon nanotube, high throughput
Topics:
Carbon nanotubes
Cite this article:
Ji Z-H, Zhang L, Tang D-M, et al. High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes. Nano Research, 2021, 14(12): 4610-4615. https://doi.org/10.1007/s12274-021-3387-y
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Abstract Full text Electronic supplementary material About this article

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1, 280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.


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Abstract
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Electronic supplementary material
About this article

High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes

Show Author's information Zhong-Hai Ji1,2, Lili Zhang1, Dai-Ming Tang3( ), Chien-Ming Chen4, Torbjörn E. M. Nordling4,5, Zheng-De Zhang6, Cui-Lan Ren6, Bo Da7, Xin Li1,2, Shu-Yu Guo1, Chang Liu1( ), Hui-Ming Cheng1,8
Shenyang National Laboratory for Materials Science Institute of Metal Research (IMR) Chinese Academy of SciencesShenyang 110016 China
School of Materials Science and Engineering University of Science and Technology of ChinaHefei 230026 China
International Center for Materials Nanoarchitectonics (MANA) National Institute for Materials Science (NIMS)1-1 Namiki, Tsukuba, Ibaraki 305-0044 Japan
Department of Mechanical Engineering "National Cheng Kung University", No. 1, University RoadTaiwan China
Department of Applied Physics and Electronics Umeå University 90187 Umeå, Sweden
Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800 China
Research and Services Division of Materials Data and Integrated System National Institute for Materials Science (NIMS)Ibaraki 305-0047 Japan
Tsinghua-Berkeley Shenzhen Institute (TBSI) Tsinghua UniversityShenzhen 518055 China

Abstract

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1, 280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.

Keywords: machine learning, optimization, chemical vapor deposition, single-wall carbon nanotube, high throughput

References(34)

1

Rao, R.; Pint, C. L.; Islam, A. E.; Weatherup, R. S.; Hofmann, S.; Meshot, E. R.; Wu, F. Q.; Zhou, C. W.; Dee, N.; Amama, P. B. et al. Carbon nanotubes and related nanomaterials: Critical advances and challenges for synthesis toward mainstream commercial applications. ACS Nano 2018, 12, 11756–11784.
DOI Google Scholar
2

Ding, F.; Rosén, A.; Bolton, K. Dependence of SWNT growth mechanism on temperature and catalyst particle size: Bulk versus surface diffusion. Carbon 2005, 43, 2215–2217.
DOI Google Scholar
3

Ding, F.; Bolton, K.; Rosén, A. Molecular dynamics study of SWNT growth on catalyst particles without temperature gradients. Comput. Mater. Sci. 2006, 35, 243–246.
DOI Google Scholar
4

Xu, Z. W.; Yan, T. Y.; Ding, F. Atomistic simulation of the growth of defect-free carbon nanotubes. Chem. Sci. 2015, 6, 4704–4711.
DOI Google Scholar
5

Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science. APL Mater. 2016, 4, 053208.
DOI Google Scholar
6

Kind, H.; Bonard, J. M.; Emmenegger, C.; Nilsson, L. O.; Hernadi, K.; Maillard-Schaller, E.; Schlapbach, L.; Forró, L.; Kern, K. Patterned films of nanotubes using microcontact printing of catalysts. Adv. Mater. 1999, 11, 1285–1289.
DOI
7

Cassell, A. M.; Verma, S.; Delzeit, L.; Meyyappan, M.; Han, J. Combinatorial optimization of heterogeneous catalysts used in the growth of carbon nanotubes. Langmuir 2001, 17, 260–264.
DOI Google Scholar
8

Cassell, A. M.; Ye, Q.; Cruden, B. A.; Li, J.; Sarrazin, P. C.; Ng, H. T.; Han, J.; Meyyappan, M. Combinatorial chips for optimizing the growth and integration of carbon nanofibre based devices. Nanotechnology 2003, 15, 9.
DOI Google Scholar
9

Noda, S.; Tsuji, Y.; Murakami, Y.; Maruyama, S. Combinatorial method to prepare metal nanoparticles that catalyze the growth of single-walled carbon nanotubes. Appl. Phys. Lett. 2005, 86, 173106.
DOI Google Scholar
10

Oliver, C. R.; Westrick, W.; Koehler, J.; Brieland-Shoultz, A.; Anagnostopoulos-Politis, I.; Cruz-Gonzalez, T.; Hart, A. J. Robofurnace: A semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery. Rev. Sci. Instrum. 2013, 84, 115105.
DOI Google Scholar
11

Nikolaev, P.; Hooper, D.; Perea-Lopez, N.; Terrones, M.; Maruyama, B. Discovery of wall-selective carbon nanotube growth conditions via automated experimentation. ACS Nano 2014, 8, 10214–10222.
DOI Google Scholar
12

Sugime, H.; Sato, T.; Nakagawa, R.; Cepek, C.; Noda, S. Gd-enhanced growth of multi-millimeter-tall forests of single-wall carbon nanotubes. ACS Nano 2019, 13, 13208–13216.
DOI Google Scholar
13

Hasegawa, K.; Noda, S. Millimeter-tall single-walled carbon nanotubes rapidly grown with and without water. ACS Nano 2011, 5, 975–984.
DOI Google Scholar
14

Chen, Z. M.; Kim, D. Y.; Hasegawa, K.; Noda, S. Methane-assisted chemical vapor deposition yielding millimeter-tall single-wall carbon nanotubes of smaller diameter. ACS Nano 2013, 7, 6719–6728.
DOI Google Scholar
15

Kluender, E. J.; Hedrick, J. L.; Brown, K. A.; Rao, R.; Meckes, B.; Du, J. S.; Moreau, L. M.; Maruyama, B.; Mirkin, C. A. Catalyst discovery through megalibraries of nanomaterials. Proc. Natl. Acad. Sci. USA 2019, 116, 40–45.
DOI Google Scholar
16

Nikolaev, P.; Hooper, D.; Webber, F.; Rao, R.; Decker, K.; Krein, M.; Poleski, J.; Barto, R.; Maruyama, B. Autonomy in materials research: A case study in carbon nanotube growth. npj Comput. Mater. 2016, 2, 16031.
DOI Google Scholar
17

Abad, S. N. K.; Ganjeh, E.; Zolriasatein, A.; Shabani-Nia, F.; Siadati, M. H. Predicting carbon nanotube diameter using artificial neural network along with characterization and field emission measure­ment. Iran. J. Sci. Technol. Trans. A: Sci. 2017, 41, 151–163.
DOI Google Scholar
18

Iakovlev, V. Y.; Krasnikov, D. V.; Khabushev, E. M.; Kolodiazhnaia, J. V.; Nasibulin, A. G. Artificial neural network for predictive synthesis of single-walled carbon nanotubes by aerosol CVD method. Carbon 2019, 153, 100–103.
DOI Google Scholar
19

Chang, J.; Nikolaev, P.; Carpena-Núñez, J.; Rao, R.; Decker, K.; Islam, A. E.; Kim, J.; Pitt, M. A.; Myung, J. I.; Maruyama, B. Efficient closed-loop maximization of carbon nanotube growth rate using bayesian optimization. Sci. Rep. 2020, 10, 9040.
DOI Google Scholar
20

Wang, H.; Yuan, Y.; Wei, L.; Goh, K.; Yu, D. S.; Chen, Y. Catalysts for chirality selective synthesis of single-walled carbon nanotubes. Carbon 2015, 81, 1–19.
DOI Google Scholar
21

Wang, J. S.; Yoo, Y.; Gao, C.; Takeuchi, I.; Sun, X. D.; Chang, H.; Xiang, X. D.; Schultz, P. G. Identification of a blue photoluminescent composite material from a combinatorial library. Science 1998, 279, 1712–1714.
DOI Google Scholar
22

Dresselhaus, M. S.; Jorio, A.; Filho, A. G. S.; Saito, R. Defect characterization in graphene and carbon nanotubes using Raman spectroscopy. Philos. Trans. Royal Soc. A: Math. Phys. Eng. Sci. 2010, 368, 5355–5377.
DOI Google Scholar
23

Stone, M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B 1974, 36, 111–133.
DOI Google Scholar
24

Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32.
DOI Google Scholar
25

Yang, F.; Wang, X.; Zhang, D. Q.; Yang, J.; Luo, D.; Xu, Z. W.; Wei, J. K.; Wang, J. Q.; Xu, Z.; Peng, F. et al. Chirality-specific growth of single-walled carbon nanotubes on solid alloy catalysts. Nature 2014, 510, 522–524.
DOI Google Scholar
26

Zhang, F.; Hou, P. X.; Liu, C.; Wang, B. W.; Jiang, H.; Chen, M. L.; Sun, D. M.; Li, J. C.; Cong, H. T.; Kauppinen, E. I. et al. Growth of semiconducting single-wall carbon nanotubes with a narrow band-gap distribution. Nat. Commun. 2016, 7, 11160.
DOI Google Scholar
27

Zhang, S. C.; Kang, L. X.; Wang, X.; Tong, L. M.; Yang, L. W.; Wang, Z. Q.; Qi, K.; Deng, S. B.; Li, Q. W.; Bai, X. D. et al. Arrays of horizontal carbon nanotubes of controlled chirality grown using designed catalysts. Nature 2017, 543, 234–238.
DOI Google Scholar
28

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R. Dubourg, V. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
Google Scholar
29
Chollet, F. Keras: The Python Deep Learning library; Astrophysics Source Code Library, 2018. https://keras.io/ (accessed Nov 9, 2020).
30

Kohonen, T. An introduction to neural computing. Neural Netw. 1988, 1, 3–16.
DOI Google Scholar
31
Nair, V.; Hinton, G. E. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp 807–814.
32
Kingma, D. P.; Ba, J. Adam: A method for stochastic optimization. 2014, arXiv: 1412.6980. arXiv. org e-Print archive. https://arxiv.org/abs/1412.6980v1 (accessed Dec 22, 2014).
33

Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273–297.
DOI Google Scholar
34

Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J.; Sheridan, R. P.; Feuston, B. P. Random forest: A classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947–1958.
DOI Google Scholar
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Publication history
Copyright
Acknowledgements

Publication history

Received: 09 November 2020
Revised: 23 January 2021
Accepted: 05 February 2021
Published: 18 March 2021
Issue date: December 2021

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Acknowledgements

Acknowledgements

The authors thank Zexin Tian and Jianqi Huang for help with training the artificial neural network model, and Shemon Baptiste and Hao-Wei "Ric" Tu for repeating and confirming the model predictions. The authors also thank Hui Li for help with AFM characterization. This project is supported by the National Key Research and Development Program of China (No. 2016YFA0200101), the National Natural Science Foundation of China (Nos. 51522210, 51972311, 51625203, 51532008, 51761135122 and 52001322), JSPS KAKENHI Grant Number JP20K05281 and JP25820336, and MOST 108-2634-F-006-009 and MOST 109-2224-E-006-003.

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