Probing the Mysterious Behavior of Tungsten as a Dopant Inside Pristine Cobalt‐Free Nickel‐Rich Cathode Materials
Bibliographic record
Abstract
Abstract Nickel‐rich cathode materials with small amounts of tungsten (W) dopants have attracted extensive attention in recent years. However, the chemical state, crystalline form, compound chemistry, and location of W in these layered cathodes are still not well‐understood. In this study, these missing structural properties are determined through a combination of macro‐, to atomic‐sensitive characterization techniques and density functional theory (DFT). W‐doped LiNiO2 (LNO) particles, prepared with mechanofusion and coprecipitation methods, are used to probe changes in the structure and location of W‐species. The results indicate that W is mainly distributed on the surfaces and inside grain boundaries of the secondary particles, regardless of the doping method. Electron energy loss spectroscopy (EELS) mapping confirms the simultaneous presence of W, O, with and without Ni in the grain boundaries as well as W‐ and O‐rich regions on the very surface. The W‐rich areas inside the grain boundaries are found to be in two forms, crystalline and amorphous. This paper suggests the presence of kinetically stabilized‐Li4+xNi1‐xWO6 (x = 0, 0.1) with the possibility of LixWyOz phases in LNO which are consistent with the electron microscopy, X‐ray absorption and diffraction data. The multiple roles of W in this complex microstructure are discussed considering the W distribution.
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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".