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

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

Volume 38 Issue 5

Oct. 2023

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Article Navigation > New Carbon Mater. > 2023 > 38(5): 887-897

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

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

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

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

GAO Zhang-dan^{1, 2
,},
JI Zhong-hai^{1, 2
,},
ZHANG Li-li^{1
,
,},
TANG Dai-ming^{3
,
,},
ZOU Meng-ke^{1, 2},
XIE Rui-hong^{1, 2},
LIU Shao-kang^{1, 2},
LIU Chang^{1
,
,}

1.
Shenyang National Laboratory for Materials Science, Institute of Metal Research (IMR), Chinese Academy of Sciences, Shenyang 110016, China
2.
School of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China
3.
Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan

More Information

Author Bio:
^†高张丹和吉忠海为共同第一作者
Corresponding author: ZHANG Li-li, Professor. E-mail: zhangll@imr.ac.cn; TANG Dai-ming, Professor. E-mail: tang.daiming@nims.go.jp; LIU Chang, Professor. E-mail: cliu@imr.ac.cn
Received Date: 2023-05-31
Accepted Date: 2023-08-24
Rev Recd Date: 2023-08-23

Available Online: 2023-08-28

Publish Date: 2023-10-01

Abstract

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 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.
- Vertically aligned carbon nanotube arrays,
- Controlled growth,
- Literature mining,
- Machine learning,
- High throughput

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References(49)

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