First-principles modeling of structural and RedOx processes in high-voltage Mn-based cathodes for sodium-ion batteries
Abstract
Sodium-ion batteries are increasingly regarded as a sustainable alternative to lithium-ion technology for large-scale energy storage, but their development remains limited by the lack of durable high-energy cathodes. Among the most promising candidates, P2–Mn-based layered oxides combine high theoretical capacity with structural versatility, yet their performance is constrained by two degradation pathways: (i) the irreversible participation of lattice oxygen in the redox process and (ii) voltage-driven solid-state phase transitions. This research article synthesizes our recent ab initio investigations aimed at disentangling the atomistic origins of these processes occurring in the high-voltage regime. We show that Mn deficiency activates oxygen redox but also promotes O2 release, whereas Fe and Ru doping strengthen TM–O covalency, enabling reversible anionic redox. In parallel, we identify cooperative Jahn–Teller distortions and Na+/vacancy reorganization as the driving forces of high-voltage phase transitions and propose simple geometric descriptors as predictive tools for structural stability. Together, these insights help to establish quantum-based design guidelines for layered sodium cathodes: reinforce TM–O covalency, suppress oxygen evolution, and mitigate phase instabilities. By combining first-principles modeling with targeted compositional design, we pave the way toward the accelerated discovery of sustainable, cobalt-free, and high-energy cathodes for next-generation sodium-ion batteries.
Acknowledgments
This study was carried out within the NEST–Network for Energy Sustainable Transition and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)–MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3). This manuscript reflects only the authors’ views and opinions; neither the European Union nor the European Commission can be considered responsible for them. Authors also acknowledge funding from the ORANGEES project (Italian Ministry of Environment and Energy Security, Research of the National Electric System PTR 2019–2021). The computing resources and the related technical support used for this work have been provided by the CRESCO/ENEA-GRID High Performance Computing infrastructure and its staff. 113 The CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, Italy, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programs; see https://www.cresco.enea.it for information.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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