A high-throughput comparative study of doped O3 and P2 cathode materials for sodium-ion batteries
Bibliographic record
Abstract
Transition metal layered oxide materials are promising candidates for sodium-ion battery cathodes. Among these, the O3 and P2 phases are the most extensively studied. While O3-phase materials are generally considered to exhibit higher capacities in full cells due to their higher sodium content, they suffer from multiple phase transitions and poor air stability. In contrast, P2-phase materials are more air-stable. Doping has shown to be beneficial to enhance both the electrochemical performance and stability of these materials. In this study we aim to determine whether doping can mitigate the inherent weaknesses of each phase and potentially enable one phase to outperform the other in both electrochemical properties and stability. This study addresses the lack of comprehensive comparative analyses between doped O3 and P2 phases. For a fair comparison, we doped 56 different elements (M) into optimal P2 (Na 0.67 Mn 0.6 Fe 0.19 Ni 0.16 M 0.05 O 2 ) and O3 (NaNi 1/3 Fe 0.28 Mn 1/3 M 0.05 O 2 ) materials. The P2 materials systematically outperform the O3 materials in terms of air stability; and doping exacerbates the contrast where some doped P2 materials (e.g. Li and Ca) show strong air stability while all O3 materials show extreme degradation in the XRD patterns. In terms of electrochemical performance, we find that cycling between 2.0 and 4.0 V favours O3 materials, but maximal capacities are in fact obtained for P2 materials cycled between 2.0 and 4.3 V, a voltage range that results in detrimental phase transitions in O3 materials, again attributed to the increased stability in the P2 materials. We estimate that in full cells, the best P2 materials will yield about 130 mAh/g while the best O3 should yield a comparable 140 mAh/g. This study demonstrates that with careful tuning, either phase holds high promise, but the inherent air stability of the P2 phases is likely to ease the path to commercialization. • Utilized high-throughput synthesis and electrochemistry to develop Na-ion battery cathodes • Optimized both doped P2 and O3 materials with 56 different dopants • O3 materials show an enhanced ability to incorporate dopants. • P2 materials show a dramatically improved stability both in air and during battery use. • Both O3 and P2 materials show high and roughly equivalent energy density.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".