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

• Published in: Nature communications Vol. 13; no. 1; p. 1424
• Main Authors: Lindsey, Rebecca K., Goldman, Nir, Fried, Laurence E., Bastea, Sorin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.03.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/563/979; 639/638/563/981; Carbon; Condensates; Growth kinetics; High pressure; Humanities and Social Sciences; multidisciplinary; Nanoclusters; Nanomaterials; Nanotechnology; Organic materials; Ostwald ripening; Precursors; Science; Science (multidisciplinary); Shock waves; Simulation; Synthesis
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29024-x

There 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. Modelling the growth of carbon nanoclusters in shock experiments is computationally demanding. Here the authors employ a machine-learned reactive interatomic model to perform large-scale simulations of nanocarbon formation from prototypical shocked C/O-containing precursor.