Transport pathways for mobile ions in disordered solids from the analysis of energy-scaled bond-valence mismatch landscapes

Literature Information

Publication Date 2009-03-18
DOI 10.1039/B901753D
Impact Factor 3.676
Authors

Stefan Adams, R. Prasada Rao


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Abstract

Structure–property relationships provide valuable guidelines for a systematic development of functional materials. Here an augmented bond-valence approach is worked out that is linked directly to the energy scale. This energy-scaled bond-valence approach is then used to identify ion-conduction pathways and to establish structure–property relationships in complex disordered solids using lithium silicate glasses as model systems. Representative local structure models of glassy solid electrolytes as a basis for the pathway analysis are derived from molecular dynamics simulations. Predictions of the bond-valence model from a static structure model are compared to a complete trajectory analysis, showing a high degree of agreement. The method yields consistent results when changing the simulation force field and is applicable to a wide range of glasses.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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