Pinpointing Chemomechanical Origins of Na Cathode Degradation
Bibliographic record
Abstract
The performance and longevity of sodium-ion batteries are heavily influenced by cathode degradation, particularly under high-voltage cycling. Despite ongoing research, the interplay between chemical and mechanical processes remains unclear. Here, we investigated the degradation mechanisms of an O3-NaLi 1/9 Ni 2/9 Fe 2/9 Mn 4/9 O 2 (NLNFM) cathode material using synchrotron-based nanoresolution chemical imaging. Oxygen loss at high voltages was identified as the primary trigger, causing unwanted phase transformations, disrupting sodium intercalation, and leading to capacity fade. Fluorine incorporation during cycling also induced stress and particle cracking, accelerating degradation. By analyzing particles of different sizes, we revealed distinct degradation pathways: small particles experience severe side reactions during early cycling due to their high specific surface area, while large particles develop progressive structural damage during extended cycling from intraparticle heterogeneity and stress. These findings highlight the particle-size-dependent nature of cathode degradation and inform strategies such as particle size optimization, doping, micromorphology design, and stress-tolerant structures to mitigate capacity fade in sodium-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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".