Unleashing the Potential of Fast Charging Batteries: Leveraging Anion Redox Chemistry in Ni- and Co-Free Cathodes
Bibliographic record
Abstract
Designing Li-ion battery cathodes free of critical raw materials such as Co and Ni has a huge technological and societal impact. Although anion redox-based Li-rich oxide cathodes allow for designing Co- and Ni-free cathode compositions, the Li-rich oxides demonstrated voltage fade, voltage hysteresis, and irreversible oxygen release despite their high capacity. Conversely, anion redox through highly covalent chalcogenides (S/Se) is emerging due to the improved covalency between metal d and ligand p bands. Here, we investigate the tuning of multichalcogen (S/Se) p band and redox-active metal d band in a model Li-rich chalcogen composition, Li 1.13 Ti 0.57 Fe 0.3 S 2– y Se y ( y = 0–1), through in-depth electrochemical, X-ray spectroscopy, and DFT-based electronic structure investigations. Introducing the appropriate amount of Se p band character in anion redox sulfides increases the interlayer distance and metal–ligand covalency without modifying the original crystal structure, promoting significant electrochemical reversibility through mixed anionic (Se 2– /Se n –, S 2– /S n –, wherein n < 2) and cationic (Fe 2+ /Fe 3+ ) redox reactions. We show the detailed Fe, S, and Se redox contributions during Li insertion–extraction through X-ray absorption (XAS) and hard X-ray photoemission spectroscopy (HAXPES) measurements. The orbital tuning approach improves the rate capability for more than 10C charge–discharge rate, exhibiting more than 50% of its original capacity obtained at the C/20 rate. The buffer cation in the lattice (Ti 4+ ) remains electrochemically inactive even after significant Se p band introduction in the sulfide framework. Overall, this work takes advantage of multianion redox chemistry to uncover practically demanding fast charging–discharging characteristics in intercalation cathodes. The obtained knowledge of this design can be extended to other oxide and chalcogen cathodes for high-performance Li-ion batteries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".