A medium-entropy transition metal oxide cathode for high-capacity lithium metal batteries
Bibliographic record
Abstract
Abstract The limited capacity of the positive electrode active material in non-aqueous rechargeable lithium-based batteries acts as a stumbling block for developing high-energy storage devices. Although lithium transition metal oxides are high-capacity electrochemical active materials, the structural instability at high cell voltages (e.g., >4.3 V) detrimentally affects the battery performance. Here, to circumvent this issue, we propose a Li1.46Ni0.32Mn1.2O4-x (0 < x < 4) material capable of forming a medium-entropy state spinel phase with partial cation disordering after initial delithiation. Via physicochemical measurements and theoretical calculations, we demonstrate the structural disorder in delithiated Li1.46Ni0.32Mn1.2O4-x, the direct shuttling of Li ions from octahedral sites to the spinel structure and the charge-compensation Mn3+/Mn4+ cationic redox mechanism after the initial delithiation. When tested in a coin cell configuration in combination with a Li metal anode and a LiPF6-based non-aqueous electrolyte, the Li1.46Ni0.32Mn1.2O4-x-based positive electrode enables a discharge capacity of 314.1 mA h g−1 at 100 mA g−1 with an average cell discharge voltage of about 3.2 V at 25 ± 5 °C, which results in a calculated initial specific energy of 999.3 Wh kg−1 (based on mass of positive electrode’s active material).
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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".