Full Concentration Gradient‐Tailored Li‐Rich Layered Oxides for High‐Energy Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Lithium‐rich layered oxides (LLOs) are prospective cathode materials for next‐generation lithium‐ion batteries (LIBs), but severe voltage decay and energy attenuation with cycling still hinder their practical applications. Herein, a series of full concentration gradient‐tailored agglomerated‐sphere LLOs are designed with linearly decreasing Mn and linearly increasing Ni and Co from the particle center to the surface. The gradient‐tailored LLOs exhibit noticeably reduced voltage decay, enhanced rate performance, improved cycle stability, and thermal stability. Without any material modifications or electrolyte optimizations, the gradient‐tailored LLO with medium‐slope shows the best electrochemical performance, with a very low average voltage decay of 0.8 mV per cycle as well as a capacity retention of 88.4% within 200 cycles at 200 mA g −1 . These excellent findings are due to spinel structure suppression, electrochemical stress optimization, and Jahn‐Teller effect inhibition. Further investigation shows that the gradient‐tailored LLO reduces the thermal release percentage by as much as about 41% when the battery is charged to 4.4 V. This study provides an effective method to suppress the voltage decay of LLOs for further practical utilization in LIBs and also puts forward a bulk‐structure design strategy to prepare better electrode materials for different rechargeable batteries.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".