新型炭材料（中英文）2023，Vol.38Issue(5)：887-897,11.DOI:10.1016/S1872-5805(23)60775-9
文献挖掘和高通量方法优化碳纳米管垂直阵列生长
Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments
摘要
Abstract
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 conductiv-ity 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 paramet-ers,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,cata-lyst 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
碳纳米管垂直阵列/控制制备/文献挖掘/机器学习/高通量Key words
Vertically aligned carbon nanotube arrays/Controlled growth/Literature mining/Machine learning/High throughput分类
化学化工引用本文复制引用
高张丹,吉忠海,张莉莉,汤代明,邹孟珂,谢蕊鸿,刘少康,刘畅..文献挖掘和高通量方法优化碳纳米管垂直阵列生长[J].新型炭材料（中英文）,2023,38(5):887-897,11.基金项目
This work was supported by the National Natur-al Science Foundation of China(51802316,51927803,52188101 and 52130209),the JSPS KAKENHI(JP20K05281 and 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). （51802316,51927803,52188101 and 52130209）
