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

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Ji, Zhong-Hai (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China; School of Materials Science and Engineering, University of Science and Technology of China, Hefei, China

Zhang, Lili (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China

Tang, Dai-Ming (author): International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, Japan

Chen, Chien-Ming (author): Department of Mechanical Engineering, National Cheng Kung University, Tainan City, Taiwan

Nordling, Torbjörn E. M. (author): Umeå universitet,Institutionen för tillämpad fysik och elektronik,Department of Mechanical Engineering, National Cheng Kung University, Tainan City, Taiwan

Zhang, Zheng-De (author): Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

Ren, Cui-Lan (author): Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

Da, Bo (author): Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Ibaraki, Japan

Li, Xin (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China; School of Materials Science and Engineering, University of Science and Technology of China, Hefei, China

Guo, Shu-Yu (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China

Liu, Chang (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China

Cheng, Hui-Ming (author): Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China; Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen, China

(creator_code:org_t)

2021-03-18
2021
English.
In: Nano Reseach. - : Tsinghua University Press. - 1998-0124 .- 1998-0000. ; 14, s. 4610-4615

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; show less...

Abstract Subject headings

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. [Figure not available: see fulltext.].

By the author/editor: Ji, Zhong-Hai; Zhang, Lili; Tang, Dai-Ming; Chen, Chien-Ming; Nordling, Torbjö ...; Zhang, Zheng-De; show more...; Ren, Cui-Lan; Da, Bo; Li, Xin; Guo, Shu-Yu; Liu, Chang; Cheng, Hui-Ming; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Physical Science ...; and Condensed Matter ...

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