Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments

doi:10.1016/S1872-5805(23)60775-9

IMR OpenIR

	Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments
	Gao, Zhang-Dan 1,2; Ji, Zhong-Hai 1,2; Zhang, Lili 1; Tang, Dai-Ming 3; Zou, Meng-Ke 1,2; Xie, Rui-Hong 1,2; Liu, Shao-Kang 1,2; Liu, Chang 1
通讯作者	Zhang, Lili(zhangll@imr.ac.cn) ; Tang, Dai-Ming(tang.daiming@nims.go.jp) ; Liu, Chang(cliu@imr.ac.cn)
	2023-10-01
发表期刊	NEW CARBON MATERIALS
ISSN	2097-1605
卷号	38 期号:5 页码:887-894
摘要	Vertically aligned carbon nanotube (VACNT) arrays with good mechanical properties and high thermal conductivity can be used as effective thermal interface materials in thermal management. In order to take advantage of the high thermal conductivity along the axis of nanotubes, the quality and height of the arrays need to be optimized. However, the immense synthesis parameter space for VACNT arrays and the interdependence of structural features make it challenging to improve both their height and quality. We have developed a literature mining approach combined with machine learning and high-throughput design to efficiently optimize the height and quality of the arrays. To reveal the underlying relationship between VACNT structures and their key growth parameters, we used random forest regression (RFR) and SHapley Additive exPlanation (SHAP) methods to model a set of published sample data (864 samples). High-throughput experiments were designed to change 4 key parameters: growth temperature, growth time, catalyst composition, and concentration of the carbon source. It was found that a screened Fe/Gd/Al2O3 catalyst was able to grow VACNT arrays with millimeter-scale height and improved quality. Our results demonstrate that this approach can effectively deal with multi-parameter processes such as nanotube growth and improve control over their structures.
关键词	Vertically aligned carbon nanotube arrays Controlled growth Literature mining Machine learning High throughput
资助者	National Natural Science Foundation of China ; JSPS KAKENHI ; Natural Science Foundation of Liaoning Province ; Liaoning Revitalization Talents Program ; Basic Research Project of Natural Science Foundation of Shandong Province, China
DOI	10.1016/S1872-5805(23)60775-9
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[51802316] ; National Natural Science Foundation of China[51927803] ; National Natural Science Foundation of China[52188101] ; National Natural Science Foundation of China[52130209] ; JSPS KAKENHI[JP20K05281] ; JSPS KAKENHI[JP25820336] ; Natural Science Foundation of Liaoning Province[2020-MS-009] ; Liaoning Revitalization Talents Program[XLYC2002037] ; Basic Research Project of Natural Science Foundation of Shandong Province, China[ZR2019ZD49]
WOS研究方向	Materials Science
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:001101668500001
出版者	ELSEVIER
引用统计	被引频次：3[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/321006/177362
专题	中国科学院金属研究所
通讯作者	Zhang, Lili; 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, Res Ctr Mat Nanoarchitecton MANA, Tsukuba, Ibaraki 3050044, Japan
推荐引用方式 GB/T 7714	Gao, Zhang-Dan,Ji, Zhong-Hai,Zhang, Lili,et al. Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments[J]. NEW CARBON MATERIALS,2023,38(5):887-894.
APA	Gao, Zhang-Dan.,Ji, Zhong-Hai.,Zhang, Lili.,Tang, Dai-Ming.,Zou, Meng-Ke.,...&Liu, Chang.(2023).Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments.NEW CARBON MATERIALS,38(5),887-894.
MLA	Gao, Zhang-Dan,et al."Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments".NEW CARBON MATERIALS 38.5(2023):887-894.