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

GAO Zhang-dan JI Zhong-hai ZHANG Li-li TANG Dai-ming ZOU Meng-ke XIE Rui-hong LIU Shao-kang LIU Chang

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. 新型炭材料(中英文), 2023, 38(5): 887-897. doi: 10.1016/S1872-5805(23)60775-9
引用本文: 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. 新型炭材料(中英文), 2023, 38(5): 887-897. doi: 10.1016/S1872-5805(23)60775-9
GAO Zhang-dan, JI Zhong-hai, ZHANG Li-li, TANG Dai-ming, ZOU Meng-ke, XIE Rui-hong, LIU Shao-kang, LIU Chang. Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments. New Carbon Mater., 2023, 38(5): 887-897. doi: 10.1016/S1872-5805(23)60775-9
Citation: GAO Zhang-dan, JI Zhong-hai, ZHANG Li-li, TANG Dai-ming, ZOU Meng-ke, XIE Rui-hong, LIU Shao-kang, LIU Chang. Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments. New Carbon Mater., 2023, 38(5): 887-897. 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

doi: 10.1016/S1872-5805(23)60775-9
详细信息
    通讯作者:

    张莉莉,博士,项目研究员. E-mail:zhangll@imr.ac.cn

    汤代明,博士,研究员. E-mail:tang.daiming@nims.go.jp

    刘 畅,博士,研究员. E-mail:cliu@imr.ac.cn

  • 中图分类号: TQ127.1+1

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

More Information
  • 摘要: 具有良好力学性能和高导热性的碳纳米管垂直阵列(VACNT)可用作热管理中的有效热界面材料。为了利用沿碳纳米管轴向的高导热性,需要优化碳纳米管垂直阵列的结晶度和高度。然而,碳纳米管垂直阵列的生长参数空间(如退火时间、催化剂种类、生长温度、载气、碳源等)复杂,结构特征之间相互影响,同时提高碳纳米管垂直阵列的高度和质量仍是一个巨大的挑战。与此同时,缺乏对参数调控方向的指导进一步增加了实验结果的不确定性,并限制了产物结构优化的效率。本研究开发了一种文献挖掘-机器学习-高通量制备策略,有效优化了碳纳米管垂直阵列的高度和质量。为了揭示碳纳米管垂直阵列结构与关键生长参数之间的潜在关系,采用随机森林回归算法对一组已发布的样本数据(864个样本)进行建模,并利用机器学习模型解释包 SHAP(SHapley Additive exPlanations)分析获得影响垂直阵列高度和结晶度的主要生长参数。经分析确定,高通量实验旨在调节4个关键参数:生长温度、生长时间、催化剂组分和碳源浓度。结果发现,经筛选的 Fe/Gd/Al2O3 催化剂能够生长出具有毫米级高度和更高结晶度的碳纳米管垂直阵列。结果表明,文献挖掘、高通量实验和基于数据的机器学习可以有效地处理碳纳米管生长等多参数过程,提高对结构的控制。
  • FIG. 2652.  FIG. 2652.

    FIG. 2652..  FIG. 2652.

    Figure  1.  Workflow for optimizing the height and quality of VACNT arrays by 4 main steps

    Figure  2.  SHAP summary plots of the effects of growth parameters in the RFR models for (a) height, and (b) IG/ID values, in descending order. All of the variables in the database constitute each point in the image, the color of the point indicates the value of the input variable, and the horizontal location shows whether the effect of that value is associated with a higher or a lower prediction. t(G.) and t(A.) denote the growth time and annealing time, respectively

    Figure  3.  Fe-Cr-Gd ternary diagram associated (a) height and (b) IG/ID values of the VACNT arrays. Plots of (c) height and (d) IG/ID values of the VACNT arrays as a function of Gd and Cr content (weight %). The arrows are just to guide the eyes

    Figure  4.  Height of VACNT arrays grown from Fe and Fe-Gd catalyst as a function of the IG/ID values

    Figure  5.  Characterizations of VACNT arrays before and after optimization. (a) Comparison of Raman spectra taken from the top of the VACNT arrays using a wavelength of 532 nm laser; (b) Cross-sectional SEM images of the VACNT arrays; TEM images and the inserted diameter distribution of VACNT arrays grown from (c) 1.8 nm Fe catalyst and (d) 1.5 nm Fe-0.2 nm Gd catalyst

    Table  1.   Regression model validation and performance evaluation

    Output
    parameters
    Regression
    model
    Performance evaluation metrics
    RMSEMAECoefficient of
    determination
    (R2)
    HeightLR0.570.330.12
    RFR0.150.090.87
    SVR0.440.190.52
    IG/IDLR2.792.440.55
    RFR2.221.640.73
    SVR2.581.970.68
    下载: 导出CSV
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  • 收稿日期:  2023-05-31
  • 录用日期:  2023-08-24
  • 修回日期:  2023-08-23
  • 网络出版日期:  2023-08-28
  • 刊出日期:  2023-10-01

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