Chemistry-mediated Ostwald ripening in carbon-rich C/O systems at extreme conditions

Author:

Lindsey Rebecca K.ORCID,Goldman Nir,Fried Laurence E.ORCID,Bastea SorinORCID

Abstract

AbstractThere is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.

Funder

DOE | LDRD | Lawrence Livermore National Laboratory

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ab initio structural dynamics of pure and nitrogen-containing amorphous carbon;Scientific Reports;2023-11-11

2. Chemical evolution in nitrogen shocked beyond the molecular stability limit;The Journal of Chemical Physics;2023-08-25

3. Modeling carbon condensation in detonation of high explosives: A tale of two approximations;SHOCK COMPRESSION OF CONDENSED MATTER - 2022: Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter;2023

4. Machine‐Learning a Solution for Reactive Atomistic Simulations of Energetic Materials;Propellants, Explosives, Pyrotechnics;2022-03-22

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