Mechanism of Action of the Tungsten Dopant in LiNiO<sub>2</sub> Positive Electrode Materials
Bibliographic record
Abstract
Abstract The addition of tungsten has been reported to greatly improve the capacity retention of Ni‐rich layered oxide cathode materials in lithium‐ion batteries. In this work, Ni(OH) 2 precursors, coated with WO 3 and also W‐containing precursors prepared by co‐precipitation followed by heat treatment with LiOH·H 2 O, are studied. Structural analysi s and electron microscopy show that W is incorporated as amorphous Li x W y O z phases concentrated in all the grain boundaries between the primary particles of LiNiO 2 (LNO) and on the surface of the secondary particles. Tungsten does not substitute for Ni or Li in the LNO lattice no matter how W is added at the precursor synthesis stage. Scanning electron microscopy (SEM) images show that adding W greatly suppresses primary particle growth during synthesis. In agreement with previous literature reports, cycling test results show that 1% W added to LNO can greatly improve charge–discharge capacity retention while also delivering a high specific capacity. The Li x W y O z amorphous phases act as coating layer on both the primary and secondary particles, restrict primary particle growth during synthesis and increase the resistance of the secondary particles to microcracking.
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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) | 0.002 | 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".