0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessInorganic solid-state ionic conductors with high ionic conductivity are of great interest for their application in safe and high-energy-density solid-state batteries. Our previous study reveals that the crystal structure of the ionic conductor Li7P3S11 contains a body-centered-cubic (bcc) arrangement of sulfur anions and that such a bcc anion framework facilitates high ionic conductivity. Here, we apply a set of first-principles calculations techniques to investigate A7P3X11-type (A = Li, Na; X = O, S, Se) lithium and sodium superionic conductors derived from Li7P3S11, focusing on their structural, dynamic and thermodynamic properties. We find that the ionic conductivity of Na7P3S11 and Na7P3Se11 is over 10 mS cm–1 at room temperature, significantly higher than that of any known solid Na-ion sulfide or selenide conductor. However, thermodynamic calculations suggest that the isostructural sodium compounds may not be trivial to synthesize, which clarifies the puzzle concerning the experimental problems in trying to synthesize these compounds.
Yan Wang, William D. Richards, Shou‐Hang Bo, Lincoln J. Miara, Gerbrand Ceder (2017). Computational Prediction and Evaluation of Solid-State Sodium Superionic Conductors Na<sub>7</sub>P<sub>3</sub>X<sub>11</sub> (X = O, S, Se). , 29(17), DOI: https://doi.org/10.1021/acs.chemmater.7b02476.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2017
Authors
5
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1021/acs.chemmater.7b02476
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access