<i>(Invited)</i> Mechanofusion-Derived Ti Modification for Stabilizing Sodium-Ion Layered Oxide Cathodes
Bibliographic record
Abstract
Surface coating and doping are critical for stabilizing layered oxide cathodes by mitigating side reactions and improving cycle life. However, conventional deposition techniques such as aqueous coating and atomic layer deposition introduce environmental concerns, high costs, and potential structural degradation, particularly for air-sensitive sodium-ion battery (SIB) cathodes. In this study, we employ an all-dry mechanofusion method to modify hydroxide precursors with a TiO₂ coating prior to sintering with a sodium source. Post-sintering, the TiO₂ surface layer undergoes in situ transformation into Ti doping, leading to enhanced Ni oxidation homogeneity, modified charge compensation, and improved thermal stability. Structural and electronic characterization via X-ray diffraction, neutron diffraction, solid-state NMR, X-ray absorption spectroscopy, and transmission X-ray microscopy confirm these modifications at the atomic and nanoscale levels. Electrochemical testing reveals enhanced capacity retention, rate capability, and air stability, with pouch cells retaining >85% capacity after 650 cycles. This scalable, environmentally sustainable approach provides a promising avenue for next-generation high-performance SIB cathodes.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".