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A New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics

preprint
submitted on 08.11.2019 and posted on 20.11.2019 by xiaorui tong, William Bowman, Alejandro Mejia-Giraldo, Peter Crozier, David Mebane

A new data-driven interacting-defect model has quantitatively described the nanoscopic com- position of high solute concentrations at grain boundaries in ion-conducting ceramics. The successful model is a data-driven Cahn-Hilliard methodology for interfaces and surfaces, introduced and demonstrated in this report. The model is applied to high spatial resolu- tion composition data gathered at grain boundaries in calcium-doped ceria. The statistical methodology for the data-driven procedure shows definitively that gradient terms are re- quired to quantitatively describe the local grain boundary composition data. The model additionally shows co-accumulation of negatively-charged acceptor dopants and positively- charged oxygen vacancies at the interface, which is qualitatively in accordance with atom probe tomography evidence in acceptor-doped ceria.

Funding

NSF CBET-1705397

NSF DMR-1308085

NSF DGE-1211230

History

Email Address of Submitting Author

david.mebane@mail.wvu.edu

Institution

West Virginia University

Country

USA

ORCID For Submitting Author

0000-0002-2198-7276

Declaration of Conflict of Interest

no conflict of interest

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