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Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations
Center for Multidimensional Carbon Materials (CMCM), Institute for Basic Science (IBS), 44919, Ulsan, Republic of Korea.ORCID iD: 0000-0003-1542-6170
Center for Multidimensional Carbon Materials (CMCM), Institute for Basic Science (IBS), 44919, Ulsan, Republic of Korea; School of Engineering, RMIT University, 3001, Victoria, Australia.
Aix-Marseille Univ, CNRS, CINaM, UMR7325, 13288, Marseille, France.ORCID iD: 0000-0002-7226-3155
Department of Mechanical Engineering, The University of Tokyo, 113-8656, Tokyo, Japan.ORCID iD: 0000-0003-3694-3070
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2024 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 15, article id 4076Article in journal (Refereed) Published
Abstract [en]

Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 15, article id 4076
National Category
Physical Chemistry
Research subject
Applied Physics
Identifiers
URN: urn:nbn:se:ltu:diva-105621DOI: 10.1038/s41467-024-47999-7ISI: 001222925300035PubMedID: 38744824Scopus ID: 2-s2.0-85193205567OAI: oai:DiVA.org:ltu-105621DiVA, id: diva2:1861096
Note

Validerad;2024;Nivå 2;2024-07-04 (joosat);

Full text license: CC BY 4.0;

Funder: Institute for Basic Science Korea (IBS-R019-D1); Japan Society for the Promotion of Science (JP23H00174, JP23H00163, JP23H05443); French Agence Nationale de la Recherche (ANR-20-CE09-0007-01); Japan Science and Technology Agency CREST (JPMJCR20B5); Swedish Research Council (2018-05973); Swedish National Infrastructure for Computing (SNIC 2022/5-110);

Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-11-20Bibliographically approved

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Larsson, J. Andreas

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