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<p>Anionic redox chemistry has emerged as a promising strategy to enhance the capacity of sodium-ion battery (SIB) cathode materials by leveraging both cationic and anionic redox processes. While anionic redox offers the potential for higher capacities, its practical implementation is often limited by challenges such as irreversibility, molecular oxygen release, and structural degradation at elevated voltages. In this study, we examine a series of hypothetical prototype cathode materials such as NaAlO<sub>2</sub>, Na<sub>2</sub>TiO<sub>3</sub>, Na<sub>2</sub>NiO<sub>3</sub>, and Na<sub>2</sub>MnO<sub>3</sub>, using a funnel-based screening approach. Machine learning interatomic potentials are employed to efficiently pre-screen a wide range of structures with different vacancy orderings, enabling the identification of low-energy configurations. These candidate structures are subsequently refined through first-principles calculations based on density functional theory (DFT). To probe the underlying anionic redox mechanisms, we apply both the PBE and SCAN meta-GGA functionals, capturing the behaviour in iono-covalent and strongly covalent Na-ion systems. We define and evaluate key descriptors to quantitatively characterize oxygen redox activity, including changes in the occupancy of metal-<i>d</i> and oxygen-<i>p</i> orbitals, the number of holes generated in these states, and shifts in average net atomic charges. Additionally, we calculate the electronic structure, integrated Crystal Orbital Hamilton Population (COHP) and average operating voltage, using the SCAN functional. This comprehensive investigation offers valuable insights into tuning metal-oxygen bonding to achieve reversible anionic redox, facilitating the rational design of next-generation, high-capacity sodium-ion cathodes.</p> Graphical abstract <p></p>

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Understanding the effect of metal-oxygen bond covalency on anionic redox activity of Na-ion-based cathode materials through first-principles

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • Priti Singh,
  • Sahil Kumar,
  • Mudit Dixit

摘要

Anionic redox chemistry has emerged as a promising strategy to enhance the capacity of sodium-ion battery (SIB) cathode materials by leveraging both cationic and anionic redox processes. While anionic redox offers the potential for higher capacities, its practical implementation is often limited by challenges such as irreversibility, molecular oxygen release, and structural degradation at elevated voltages. In this study, we examine a series of hypothetical prototype cathode materials such as NaAlO2, Na2TiO3, Na2NiO3, and Na2MnO3, using a funnel-based screening approach. Machine learning interatomic potentials are employed to efficiently pre-screen a wide range of structures with different vacancy orderings, enabling the identification of low-energy configurations. These candidate structures are subsequently refined through first-principles calculations based on density functional theory (DFT). To probe the underlying anionic redox mechanisms, we apply both the PBE and SCAN meta-GGA functionals, capturing the behaviour in iono-covalent and strongly covalent Na-ion systems. We define and evaluate key descriptors to quantitatively characterize oxygen redox activity, including changes in the occupancy of metal-d and oxygen-p orbitals, the number of holes generated in these states, and shifts in average net atomic charges. Additionally, we calculate the electronic structure, integrated Crystal Orbital Hamilton Population (COHP) and average operating voltage, using the SCAN functional. This comprehensive investigation offers valuable insights into tuning metal-oxygen bonding to achieve reversible anionic redox, facilitating the rational design of next-generation, high-capacity sodium-ion cathodes.

Graphical abstract