Preventing Interdiffusion during Synthesis of Ni-Rich Core–Shell Cathode Materials
Bibliographic record
Abstract
Surface reactions between Ni-rich cathode materials and electrolytes limit the achievable specific capacity and lifetime in the high energy density Li-ion batteries based on these cathode materials. A core–shell approach, which contains a less reactive shell-phase on top of a high-capacity core-phase, can be used to reduce these surface reactions. However, interdiffusion of the elements in the core and shell phases can occur during calcination, which limits the choice of elements to be used in the shell phase and the temperature window of synthesis, and often increases the minimum shell thickness. Tungsten oxide (WO 3 ) coating on the surface of precursors leads to the formation of Li x W y O z secondary phases during the heat treatment with LiOH·H 2 O. These Li x W y O z phases infuse into the grain boundaries and prevent interdiffusion between the core and shell phases. Tungsten-containing Ni-rich core–shell cathode materials with Mn- or Al-based shells show enhanced electrochemical performance because of reduced surface reactivity due to the core–shell microstructure and additional mechanical strength owing to the presence of Li x W y O z phases in the grain boundaries.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".