Impact of Shell Composition, Thickness and Heating Temperature on the Performance of Nickel-Rich Cobalt-Free Core-Shell Materials
Bibliographic record
Abstract
Ni-rich lithium transition metal oxides have high specific capacity but generally have inferior cycling performance compared to their lower Ni content counterparts. core–shell structures with a Ni-rich core and a Mn-containing shell have been reported to improve the cycling performance of Ni-rich materials, but the impact of the shell on the performance of core–shell materials needs to be elaborated more. In this work, three core–shell precursors having a Ni(OH)2 core, but different shell compositions and thicknesses, were lithiated at various temperatures and the resulting materials were examined physically and electrochemically. They were compared to the corresponding uniform “shell” materials lithiated at the same temperatures. The selection of heating temperature is crucial and must be made with care to limit the interdiffusion between core and shell compositions while still heating to sufficient temperature to prepare crystalline materials with little lithium in the transition metal layer. Once these factors are understood, core–shell structures with an optimized shell thickness and Mn content can be made to simultaneously achieve high specific capacity and long cycle life.
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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".