Abstract
P2-type cobalt-free Na0.67Ni0.33Mn0.67O2 (NNMO) compound has attracted significant attention as a cathode material for sodium-ion batteries, due to its relatively high energy density. Recent experimental investigations on doped NNMO cathodes have demonstrated improved structural stability, enhanced electrical and ionic conductivity of Na+ ions, and increased capacity and cycling stability. In this study, we investigate the effects of doping on NNMO cathode materials across different charge states. Six different dopants- Mg2+, Al3+, Ti4+, Zr4+, Nb5+, and Mo6+ were selected based on their oxidation states, and their impact on NNMO was evaluated using Machine Learning Potentials and Density Functional Theory (DFT) calculations. Key properties, including lattice parameters, redox activity, voltage activity, and Na+ diffusion kinetics were computed and analysed for each doped variant of the NNMO cathode. The results of these detailed investigations show that higher valence dopants (Zr4+, Nb5+, and Mo6+) primarily enhance the bulk stability while preserving high energy density; whereas, lower valence dopants contribute more significantly to structural integrity, voltage regulation, and ion transport. Each dopant improves certain aspects of structural stability, surface robustness, voltage window, Na+ diffusion, and overall electrochemical performance. Furthermore, a correlation between the theoretical predictions and experimental results for specific variables (dopant oxidation states and ionic radii) is clearly established, offering an essential guideline for choosing a suitable dopant for superior NNMO cathode design.
| Original language | English |
|---|---|
| Journal | Small |
| Early online date | 21 Jan 2026 |
| DOIs | |
| State | E-pub ahead of print - 21 Jan 2026 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2026 Wiley-VCH GmbH.
Keywords
- bond valence site energy
- layered oxide
- machine learning potentials
- monte carlo simulated annealing
- sodium ion batteries
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