Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning-Reference-Cited by-同舟云学术

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Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning

Published:2023-11-15 Issue:22 Volume:17 Page:22821-22829
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Lin Dewu¹,Muroga Shun¹^ORCID,Kimura Hiroe²,Jintoku Hirokuni¹^ORCID,Tsuji Takashi¹^ORCID,Hata Kenji¹,Chen Guohai¹^ORCID,Futaba Don N.¹^ORCID

Affiliation:

1. Nano Carbon Device Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 5, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8565, Japan

2. Department of Engineering for Future Innovation, National Institute of Technology, Ichinoseki College, Takanashi, Hagisho, Ichinoseki, Iwate 021-8511, Japan

Funder

Japan Society for the Promotion of Science

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.3c07587

Reference49 articles.

1. Carbon Nanotubes: Present and Future Commercial Applications

2. Application-Driven Carbon Nanotube Functional Materials

3. Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications

4. Mass production of aligned carbon nanotube arrays by fluidized bed catalytic chemical vapor deposition

5. The large-scale production of carbon nanotubes in a nano-agglomerate fluidized-bed reactor

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