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Published on: July 19, 2019
First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs
Shenli Zhang1,2, Andrew J E Rowberg1,2, ShinYoung Kang1,2
1Quantum Simulations Group, Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
Researchers developed a simplified materials model to accelerate the discovery of proton-conducting oxides (PCOs). Strong metal-oxide bonds and specific cation properties significantly reduce proton hopping barriers, aiding in designing efficient energy materials.
Area of Science:
- Materials Science
- Computational Chemistry
- Energy Conversion Technologies
Background:
- Proton-conducting oxides (PCOs) are crucial for energy conversion, but their optimization is complex.
- Current PCO research often involves intricate perovskite oxides requiring doping and alloying.
- The vast chemical diversity in oxides challenges efficient materials design.
Purpose of the Study:
- To establish a simplified materials motif database for understanding proton kinetics in oxides.
- To elucidate the fundamental relationship between materials chemistry and proton conductivity.
- To enable rational design and rapid screening of novel PCOs.
Main Methods:
- Utilized a computational approach focusing on the zincblende structure as a proxy for tetrahedral metal-oxide coordination.
- Systematically quantified the impact of cation type, oxidation states, and M-O bond lengths on proton hopping barriers.
- Mapped identified cation-geometry combinations onto existing materials databases (ICSD, Materials Project).
Main Results:
- Identified that strong M-O bonds and metal cations with large, variable oxidation states (e.g., Mo6+, V5+) correlate with lower proton hopping barriers.
- Successfully identified real materials containing predicted metal-oxide units within existing databases.
- Demonstrated good agreement between calculated proton hopping barriers in real structures and predictions from the motif database.
Conclusions:
- The simplified motif model provides a valuable first step towards the rational design of energy-efficient PCOs.
- The findings facilitate a quicker screening process for potential PCO candidates.
- Acknowledged model limitations and outlined future extensions for enhanced predictive accuracy.
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