Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields

The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFFs), which can study the interface dynamics under near ab initio accuracy, provide a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-carbon MLFF and perform growth simulations on a cobalt catalyst to investigate the chirality preference of the growth of SWCNTs under the vapor-liquid-solid (VLS) regime. Through microkinetic modeling, we reproduce the observed growth and defect kinetics, demonstrating their dependence on the chirality. It is observed that while the initial chirality assignment is likely related to the configurational degeneracy of the nanotube caps, pentagon defects immediately form and resolve after nucleation. Such processes, which we name diameter control mechanisms, not only control the diameter toward an optimum but also shift the chirality distribution drastically. Our simulation shows a preference toward the (6,5) chirality, which is also widely observed experimentally. Our work offers a microkinetic modeling workflow for the chirality-dependent kinetics of the SWCNTs, highlighting the important contribution of the defect kinetics to the chirality origination.

Authors

Institutions

Publication Details

Journal
Journal of the American Chemical Society
Published
2025-02-15
DOI
https://doi.org/10.1021/jacs.4c18769
Citations
9
Primary Topic
Carbon Nanotubes in Composites
Type
article
Field-Weighted Citation Impact
4.66

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields

Shigeo Maruyama, Yan Li, Sida Sun
9 citations
Journal of the American Chemical Society
Carbon Nanotubes in Composites
4.66
article

Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields

Shigeo Maruyama, Yan Li, Sida Sun
article en
9 citations

Abstract

The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFFs), which can study the interface dynamics under near ab initio accuracy, provide a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-carbon MLFF and perform growth simulations on a cobalt catalyst to investigate the chirality preference of the growth of SWCNTs under the vapor-liquid-solid (VLS) regime. Through microkinetic modeling, we reproduce the observed growth and defect kinetics, demonstrating their dependence on the chirality. It is observed that while the initial chirality assignment is likely related to the configurational degeneracy of the nanotube caps, pentagon defects immediately form and resolve after nucleation. Such processes, which we name diameter control mechanisms, not only control the diameter toward an optimum but also shift the chirality distribution drastically. Our simulation shows a preference toward the (6,5) chirality, which is also widely observed experimentally. Our work offers a microkinetic modeling workflow for the chirality-dependent kinetics of the SWCNTs, highlighting the important contribution of the defect kinetics to the chirality origination.

Journal of the American Chemical SocietyVol. 147(8)
Shanxi University (CN), Peking University (CN), Shanxi University of Traditional Chinese Medicine (CN), Advanced Materials and Devices (United States) (US), The University of Tokyo (JP)
National Natural Science Foundation of China, National Key Research and Development Program of China, Beijing National Laboratory for Molecular Sciences
Openalex Percentile: Top 5%
Carbon Nanotubes in Composites
4.66
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.