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Christophe Bichara

Aix-Marseille University

3 Avenue Robert Schuman

3 Avenue Robert Schuman,

Aix-en-Provence, 13628

France

  • Aix-Marseille University

SCHOLARLY PAPERS

2

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35

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0

Scholarly Papers (2)

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1.

Edge Dynamics in Iron-Cluster Catalyzed Growth of Single-Walled Carbon Nanotubes Revealed by Molecular Dynamics Simulations Based on a Neural Network Potential

Number of pages: 36 Posted: 09 Aug 2025
Ikuma Kohata, Ryo Yoshikawa, Kaoru Hisama, Christophe Bichara, Keigo Otsuka and Shigeo Maruyama
affiliation not provided to SSRN, affiliation not provided to SSRN, Shinshu University, Aix-Marseille University, University of Tokyo - The University of Tokyo and University of Tokyo
Downloads 19 (1,459,862)

Abstract:

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carbon nanotube, Iron catalyst, Machine-learning interatomic potential, Molecular dynamics simulation

2.

Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes

Number of pages: 31 Posted: 06 Apr 2026
Qingmei Hu, Daniel Hedman, Ya Feng, Wanyu Dai, Daisuke Asa, Aina Fitó-Parera, Yixi Yao, Yongjia Zheng, Kaoru Hisama, Gunjan Auti, Hirofumi Daiguji, Shohei Chiashi, D. I. Levshov, Wim Wenseleers, Keigo Otsuka, Yan Li, Christophe Bichara, Sofie Cambré, Rong Xiang and Shigeo Maruyama
University of Tokyo - The University of Tokyo, affiliation not provided to SSRN, University of Tokyo, University of Tokyo - The University of Tokyo, University of Tokyo - The University of Tokyo, University of Antwerp, Peking University, Zhejiang University, Shinshu University, University of Tokyo - The University of Tokyo, University of Tokyo - Department of Mechanical Engineering, University of Tokyo, University of Antwerp, University of Antwerp, University of Tokyo - The University of Tokyo, Peking University, Aix-Marseille University, University of Antwerp, Zhejiang University and University of Tokyo
Downloads 16 (1,526,714)

Abstract:

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Bimetallic Catalysts, single-walled carbon nanotubes, Chemical Vapor Deposition, Optical Absorption, Fluorescence, Machine Learning Molecular Dynamics