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Research Article

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

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
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Graphical Abstract

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 learningoptimizationchemical vapor depositionsingle-wall carbon nanotubehigh throughput

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Nano Research
Volume 14 Issue 12,
December 2021
Pages 4610-4615
DOI: 10.1007/s12274-021-3387-y
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
Topics:
Carbon nanotubes

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Received: 09 November 2020
Revised: 23 January 2021
Accepted: 05 February 2021
Published: 18 March 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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