High-throughput study examining the wide benefit of Li substitution in oxide cathodes for Na-ion batteries
Bibliographic record
Abstract
The stability of Na-ion cathodes is crucial for the widespread adoption of these sustainable battery technologies. Air stability, a critical factor, impacts the synthesis, storage, electrochemical performance, and safety of cathode materials from laboratory research through to commercial manufacturing. Poor air stability of these cathodes leads to disastrous electrochemical performance. Despite significant advancement in understanding air stability of these materials, a comprehensive strategy to universally enhance the air stability of layered oxides remains undeveloped. Here, we use high-throughput methods to systematically study the impact of substituting Li into 24 different sodium layered oxides. The compositions include all of the currently studied structures such as P2, P3 and O3. From a structural point of view, this substitution is quite facile and generally Li integrates smoothly into the structures. Remarkably, lithium strongly enhances air stability across all 24 compositions as determined from the large set of 96 XRD patterns. The improvement in air stability is thus established as a universal benefit of lithium substitution. While a number of Li-doped samples show lower capacities than in their undoped counterparts, two Li-doped samples demonstrate both improved electrochemical performance and complete air stability under harsh aging conditions.
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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 | 0.001 |
| 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".