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

doi:10.1007/s12274-021-3387-y

IMR OpenIR

	High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
	Ji, Zhong-Hai 1,2; Zhang, Lili 1; Tang, Dai-Ming 3; Chen, Chien-Ming 4; Nordling, Torbjorn E. M.4,5; Zhang, Zheng-De 6; Ren, Cui-Lan 6; Da, Bo 7; Li, Xin 1,2; Guo, Shu-Yu 1; Liu, Chang 1; Cheng, Hui-Ming 1,8
通讯作者	Tang, Dai-Ming(tang.daiming@nims.go.jp) ; Liu, Chang(cliu@imr.ac.cn)
	2021-03-18
发表期刊	NANO RESEARCH
ISSN	1998-0124
页码	6
摘要	It 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.
关键词	single-wall carbon nanotube high throughput machine learning optimization chemical vapor deposition
资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; JSPS KAKENHI
DOI	10.1007/s12274-021-3387-y
收录类别	SCI
语种	英语
资助项目	National 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研究方向	Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
WOS类目	Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000630681600001
出版者	TSINGHUA UNIV PRESS
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/161423
专题	中国科学院金属研究所
通讯作者	Tang, Dai-Ming; Liu, Chang
作者单位	1.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
推荐引用方式 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.