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High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
Ji, Zhong-Hai1,2; Zhang, Lili1; Tang, Dai-Ming3; Chen, Chien-Ming4; Nordling, Torbjorn E. M.4,5; Zhang, Zheng-De6; Ren, Cui-Lan6; Da, Bo7; Li, Xin1,2; Guo, Shu-Yu1; Liu, Chang1; Cheng, Hui-Ming1,8
Corresponding AuthorTang, Dai-Ming(tang.daiming@nims.go.jp) ; Liu, Chang(cliu@imr.ac.cn)
2021-03-18
Source PublicationNANO RESEARCH
ISSN1998-0124
Pages6
AbstractIt has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Hem, a high-throughput method combined with machine leaming 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 (I-G/I-D) 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.
Keywordsingle-wall carbon nanotube high throughput machine learning optimization chemical vapor deposition
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; JSPS KAKENHI
DOI10.1007/s12274-021-3387-y
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFA0200101] ; National Natural Science Foundation of China[51522210] ; National Natural Science Foundation of China[51972311] ; National Natural Science Foundation of China[51625203] ; National Natural Science Foundation of China[51532008] ; National Natural Science Foundation of China[51761135122] ; National Natural Science Foundation of China[52001322] ; JSPS KAKENHI[JP20K05281] ; JSPS KAKENHI[JP25820336] ; [MOST 108-2634-F-006-009] ; [MOST 109-2224-E-006-003]
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
WOS SubjectChemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000630681600001
PublisherTSINGHUA UNIV PRESS
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/161424
Collection中国科学院金属研究所
Corresponding AuthorTang, Dai-Ming; Liu, Chang
Affiliation1.Chinese Acad Sci, Inst Met Res IMR, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China
2.Univ Sci & Technol China, Sch Mat Sci & Engn, Hefei 230026, Peoples R China
3.Natl Inst Mat Sci NIMS, Int Ctr Mat Nanoarchitecton MANA, 1-1 Namiki, Tsukuba, Ibaraki 3050044, Japan
4.Natl Cheng Kung Univ, Dept Mech Engn, 1 Univ Rd, Tainan 701, Taiwan
5.Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden
6.Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
7.Natl Inst Mat Sci NIMS, Res & Serv Div Mat Data & Integrated Syst, Ibaraki 3050047, Japan
8.Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst TBSI, Shenzhen 518055, Peoples R China
Recommended Citation
GB/T 7714
Ji, Zhong-Hai,Zhang, Lili,Tang, Dai-Ming,et al. High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes[J]. NANO RESEARCH,2021:6.
APA Ji, Zhong-Hai.,Zhang, Lili.,Tang, Dai-Ming.,Chen, Chien-Ming.,Nordling, Torbjorn E. M..,...&Cheng, Hui-Ming.(2021).High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes.NANO RESEARCH,6.
MLA Ji, Zhong-Hai,et al."High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes".NANO RESEARCH (2021):6.
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