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Predicting Emergence of Nanoscale Order in Surface Oxides through Preferential Interactivity Parameter.

作者信息

Martin Andrew, Thuo Martin

机构信息

Department of Materials Science and Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States.

出版信息

ACS Nano. 2024 Mar 5;18(9):6740-6747. doi: 10.1021/acsnano.3c10935. Epub 2024 Feb 14.

DOI:10.1021/acsnano.3c10935
PMID:38354032
Abstract

Diffusion and surface oxidation are critical processes in metal alloy designs and use. Surface oxides provide opportunities to improve material properties or performance beyond bulk alterations. Surface oxidation is, however, often oversimplified into a classical diffusion process. Passivating oxide surfaces are also thought to be lacking in complexity or critical information. A closer look, however, shows inherent complexity with kinetics-driven competition between the elements in the process leading to redox-speciation across a very small (nm) thickness. Questions that remain to be answered for a comprehensive understanding of surface oxides are diverse and call for interdisciplinary approaches. By using the thermodynamics-based Preferential Interactivity Parameter (PIP) alongside kinetic consideration, we show how complexity in these oxides can be predicted allowing us to tailor these thin films. We use our work, and that of others, to illustrate predictability while also highlighting that there is still much more to be done.

摘要

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