Composite Nanostructure Construction on the Grain Surface of Li‐Rich Layered Oxides
Bibliographic record
Abstract
Abstract Li‐rich layered oxides (LLOs) are fascinating high‐energy cathodes for lithium‐ion batteries (LIBs), but still suffer from critical drawbacks that retard their practical applications. Although surface modification is effective to protect LLOs from structural deterioration, the delicate design of structures on a grain surface with promising scalability for industrial application is still challenging. Herein, using the atomic layer deposition (ALD) technique, a composite nanostructure comprising a uniform LiTaO3 coating layer (≈3 nm) and a spinel interlayer structure (≈1 nm) is constructed on the grain surface of industrial LLO (Li1.13Mn0.517Ni0.256Co0.097O2) agglomerated spheres. The surface composite nanostructure can not only enhance the structural/interfacial stability of the LLO, but also facilitates Li+ diffusion, thereby significantly improving its cycle stability, rate performance, thermal stability, and voltage maintenance. Specifically, the LLO coated with 10 ALD cycles exhibits a small voltage decay rate of 0.9 mV per cycle, a reversible capacity of 272.8 mAh g−1 at 0.1 C, and a capacity retention of 85% after 200 cycles at 1 C, suggesting the important role of surface composite nanostructure for improving the electrochemical performance. This work provides new insights into the composite nanostructure design on the grain surface of cathode materials for high‐performance LIBs.
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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) | 0.000 | 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 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".