Stabilization of Na‐Ion Cathode Surfaces: Combinatorial Experiments with Insights from Machine Learning Models
Why this work is in the frame
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Bibliographic record
Abstract
Na–Fe–Mn–O cathodes hold promise for environmentally benign high‐energy sodium‐ion batteries, addressing material scarcity concerns in Li‐ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high‐throughput experiments to make hundreds of substitutions into a previously optimized Na–Fe–Mn–O material are utilized. Numerous single‐phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. Machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next‐generation sodium‐ion batteries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it