Molecular dynamics simulations of the lithium coordination environment in phosphate glasses

Literature Information

Publication Date 2000-09-15
DOI 10.1039/B004627M
Impact Factor 3.676
Authors

Todd M. Alam, Jian-Jie Liang, Randall T. Cygan


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Abstract

A molecular dynamics (MD) study of the lithium ultraphosphate glass series, xLi2O·(1−x)P2O5 (0⩽x⩽0.5) was used to investigate the changes in the Li environment with increasing modifier concentration. The results from the MD simulations indicate that only gradual structural variations in the Li coordination environment occur as a function of modifier content. Changes in the type of oxygen coordinated to the Li are observed to correlate with the minimum in the glass transition temperature. Additionally, changes in the number of shared phosphorus vertices were seen with increasing modifier concentration, in support of recent models involving the role of the cation modifier in the extended range structure of phosphate glasses. Empirical calculations of the 6Li NMR chemical shifts directly from the MD simulation structures are also reported and compared to recent experimental solid-state NMR results.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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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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