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

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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.

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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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Authors and Affiliations

  1. Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, 110016, China

    Zhong-Hai Ji, Lili Zhang, Xin Li, Shu-Yu Guo, Chang Liu & Hui-Ming Cheng

  2. School of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, China

    Zhong-Hai Ji & Xin Li

  3. International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan

    Dai-Ming Tang

  4. Department of Mechanical Engineering, National Cheng Kung University, No. 1, University Road, Tainan City, 701, Taiwan

    Chien-Ming Chen & Torbjörn E. M. Nordling

  5. Department of Applied Physics and Electronics, Umeå University, 90187, Umeå, Sweden

    Torbjörn E. M. Nordling

  6. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China

    Zheng-De Zhang & Cui-Lan Ren

  7. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Ibaraki, 305-0047, Japan

    Bo Da

  8. Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen, 518055, China

    Hui-Ming Cheng

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  1. Zhong-Hai Ji
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  2. Lili Zhang
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  3. Dai-Ming Tang
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  4. Chien-Ming Chen
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  6. Zheng-De Zhang
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  9. Xin Li
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  10. Shu-Yu Guo
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  11. Chang Liu
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Correspondence to Dai-Ming Tang or Chang Liu.

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Ji, ZH., Zhang, L., Tang, DM. et al. High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes. Nano Res. 14, 4610–4615 (2021). https://doi.org/10.1007/s12274-021-3387-y

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  • DOI: https://doi.org/10.1007/s12274-021-3387-y

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