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

文献类型：期刊论文

作者Gao, Zhang-Dan2,3; Ji, Zhong-Hai2,3; Zhang, Lili3; Tang, Dai-Ming1; Zou, Meng-Ke2,3; Xie, Rui-Hong2,3; Liu, Shao-Kang2,3; Liu, Chang3
刊名NEW CARBON MATERIALS
出版日期2023-10-01
卷号38期号:5页码:887-894
关键词Vertically aligned carbon nanotube arrays Controlled growth Literature mining Machine learning High throughput
ISSN号2097-1605
DOI10.1016/S1872-5805(23)60775-9
通讯作者Zhang, Lili(zhangll@imr.ac.cn) ; Tang, Dai-Ming(tang.daiming@nims.go.jp) ; Liu, Chang(cliu@imr.ac.cn)
英文摘要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.
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
语种英语
WOS记录号WOS:001101668500001
出版者ELSEVIER
资助机构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
源URL[http://ir.imr.ac.cn/handle/321006/177361]  
专题金属研究所_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.Natl Inst Mat Sci NIMS, Res Ctr Mat Nanoarchitecton MANA, Tsukuba, Ibaraki 3050044, Japan
2.Univ Sci & Technol China, Sch Mat Sci & Engn, Hefei 230026, Peoples R China
3.Chinese Acad Sci, Inst Met Res IMR, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China
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.

入库方式： OAI收割

来源：金属研究所

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