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

Ji, Zhong-Hai (författare)
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 (författare)
Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China
Tang, Dai-Ming (författare)
International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, Japan
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Chen, Chien-Ming (författare)
Department of Mechanical Engineering, National Cheng Kung University, Tainan City, Taiwan
Nordling, Torbjörn E. M. (författare)
Umeå universitet,Institutionen för tillämpad fysik och elektronik,Department of Mechanical Engineering, National Cheng Kung University, Tainan City, Taiwan
Zhang, Zheng-De (författare)
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
Ren, Cui-Lan (författare)
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
Da, Bo (författare)
Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Ibaraki, Japan
Li, Xin (författare)
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 (författare)
Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China
Liu, Chang (författare)
Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang, China
Cheng, Hui-Ming (författare)
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
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 (creator_code:org_t)
2021-03-18
2021
Engelska.
Ingår i: Nano Reseach. - : Tsinghua University Press. - 1998-0124 .- 1998-0000. ; 14, s. 4610-4615
Relaterad länk:
https://urn.kb.se/re...
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https://doi.org/10.1...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.].

Ämnesord

NATURVETENSKAP  -- Fysik -- Den kondenserade materiens fysik (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences -- Condensed Matter Physics (hsv//eng)

Nyckelord

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

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