Promoting Reversible Anionic Redox in Sodium-Ion Cathodes by Doping and Phase Control
Notice bibliographique
Résumé
Sodium-ion batteries (SIBs) have emerged as promising energy storage systems due to their reliance on earth-abundant elements, environmental friendliness, and excellent electrochemical performance.[1] However, traditional cathode materials are constrained by the redox activity of transition metals, which limits achievable capacities and makes it challenging to reach high operational voltages. An innovative approach to overcome these limitations is to exploit anionic redox, specifically through oxygen in layered oxides, to access both high voltages and additional capacities.[2] While some progress has been made with Li doping on the TM layer to achieve materials like NaxLi0.25Mn0.75O2 and with model Na-rich oxides like Na2IrO3, the compositional/structural factors that favours the desirable reversible oxygen redox are not fully understood, and predictive design strategies remain elusive.[3] To address this gap, we conducted an extensive high-throughput screening of over 50 dopants across the periodic table [4, 5], examining their effects on oxygen redox activation. Our analysis revealed a significant correlation between bond valence mismatch and oxygen activity.[4] Specifically, we found that a larger bond valence mismatch induces local structural distortions in the layered oxides, destabilizing the non-bonding O-2p orbitals. This destabilization makes oxygen redox accessible at high voltages of approximately 4.2V and 4.5V vs. Na/Na+, with notable features of high reversibility and low overpotential. Based on this screening, we identified five dopants—K, Cu, Rb, Cs, and Tl—as particularly interesting for further study, each exhibiting a high bond valence mismatch. We then incorporated these dopants at a 10% level into Na0.66MnO2. We synthesized two polymorphs for each: one taking the P2 structure and the other the P’2 layered structures. Electrochemical testing revealed stark differences between these two phases: P2 materials demonstrated robust, reversible oxygen redox activity at high voltages, while P’2 materials showed irreversible oxygen redox. To investigate these differences at the atomic level, we employed advanced synchrotron-based techniques, including X-ray Absorption Spectroscopy (XAS), Wavelet Transform Extended X-ray Absorption Fine Structure (WT-EXAFS), and Resonant Inelastic X-ray Scattering (RIXS). These analyses confirmed that doped P2 structures effectively stabilize electron-holes on oxygen at high voltages, thus avoiding the formation of (O-O)n- dimers or trapped O2 species that can lead to irreversible structural changes. We attribute this stability to the large dopants increasing the separation between oxygens thereby preventing their interaction. WT-EXAFS further indicated that dopants play a crucial role in regulating metal migration, with Cu migration in particular stabilizing Mn within the lattice, thereby preventing irreversible structural reordering. This stabilization supports sustained oxygen redox activity and enhances the overall durability of the material. Thus, we establish the key mechanisms involved in inducing stable reversible oxygen redox in the P2 materials and thereby provide new design strategies for this emerging class of cathodes. References [1] Jia, Shipeng, Shinichi Kumakura, and Eric McCalla. "Unravelling air/moisture stability of cathode materials in sodium ion batteries: characterization, rational design, and perspectives." Energy & Environmental Science (2024). [2] McCalla, Eric, et al. "Visualization of OO peroxo-like dimers in high-capacity layered oxides for Li-ion batteries." Science 350.6267 (2015): 1516-1521. [3] Zhang, Xiaoyu, et al. "Manganese‐based Na‐rich materials boost anionic redox in high‐performance layered cathodes for sodium‐ion batteries." Advanced Materials 31.27 (2019): 1807770. [4] Jia, Shipeng, et al. "Chemical speed dating: the impact of 52 dopants in Na–Mn–O cathodes." Chemistry of Materials 34.24 (2022): 11047-11061. [5] Jia, Shipeng, et al. "Stabilization of Na‐Ion Cathode Surfaces: Combinatorial Experiments with Insights from Machine Learning Models." Advanced Energy and Sustainability Research (2024): 2400051.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».