Tungsten Infused Grain Boundaries Enabling Universal Performance Enhancement of Co-Free Ni-Rich Cathode Materials
Bibliographic record
Abstract
Ni-rich cathode materials suffer from poor capacity retention due to micro-cracking and interfacial reactivity with electrolyte. Addition of tungsten (W) to some Ni-rich materials can improve capacity retention. Here, a WO3 surface coating is applied on Ni-rich hydroxide precursors before heating with lithium hydroxide. After heating in oxygen, Ni-rich materials with any of the commonly used dopants (magnesium, aluminum, manganese, etc.) show a “universal” improvement in capacity retention. Experimental characterization and theoretical modelling showed W was concentrated in the grain boundaries between the primary grains of secondary particles of the layered oxides, and W is incorporated in amorphous LixWyOz phases rather than as a substituent in the LiNiO2 lattice. This self-infusion of W in the grain boundaries during synthesis also significantly restricts primary crystallite grain growth. Along with smaller primary grain size, the LixWyOz phases in the grain boundaries lead to improved resistance to microcracking and reduced surface or interfacial reactivity. Improving the intrinsic properties of primary grains through doping of Mg, Al, or Mn and reinforcing the secondary particle structure mechanically and chemically using W or a similar element, M, that forms LixMOy phases and does not substitute into LiNiO2 is a universal strategy to improve polycrystalline Ni-rich materials.
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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".