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arXiv · 2503.04035

Unveiling the Oxidation Mechanisms of Octa-Penta Graphene: A Multidimensional Exploration from First-Principles to Machine Learning

Chenyi Zhou·Rubin Huo·Boyi Situ·Zihan Yan·Zhe Zhang·Yusong Tu
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Abstract

Octa-penta graphene (OPG), a novel carbon allotrope characterized by its distinctive arrangement of pentagonal and octagonal rings, has garnered considerable attention due to its exceptional structure and functional properties. This study systematically investigates the oxidation mechanisms of OPG and elucidates the oxygen migration patterns on the OPG monolayer through first-principles calculations and machine-learning-based molecular dynamics (MLMD) simulations. Specifically, the oxidation processes on OPG-L and OPG-Z involve exothermic chemisorption, where oxygen molecules dissociate at the surfaces, forming stable epoxy groups. Furthermore, the integrated-crystal orbital Hamilton population (ICOHP) and Bader charge analyses provide insights into the physical mechanisms of oxygen atom adsorption. Importantly, we found that oxidation also impact the electronic properties of OPG, with OPG-L retaining its metallic characteristics post-oxygen adsorption, whereas OPG-Z undergoes a transformation from a metallic to a semiconducting state due to the introduction of oxygen. Oxygen migration on OPG monolayer involves breaking and reforming of C-O bonds, with varying stability across adsorption sites and limited migration along the basal plane. MLMD simulations corroborate these migration patterns, offering detailed migration trajectories consistent with theoretical predictions. These findings enhance the understanding of oxygen migration dynamics on OPG, facilitate its experimental validations, and highlight its potential as a novel 2D material for applications in batteries, heat-resistant materials, and oxidation-resistant coatings.

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Chenyi Zhou, Rubin Huo, Boyi Situ, Zihan Yan, Zhe Zhang, Yusong Tu. 2025-03-06. Unveiling the Oxidation Mechanisms of Octa-Penta Graphene: A Multidimensional Exploration from First-Principles to Machine Learning. https://arxiv.org/abs/2503.04035

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