Designing Surface Coating Strategies with Tungsten on Single Crystal NMC Materials by XPS
Bibliographic record
Abstract
Abstract Medium‐nickel layered oxide materials are a promising positive electrode material for high energy density lithium ion batteries. These materials can suffer from surface instability at a highly de‐lithiated state and poor rate capability. Surface coatings with multiple elements are a common strategy to overcome some of these challenges, however, the rationale behind adding these elements has never been explored. Here, various mid‐nickel single crystal vendor materials are evaluated by X‐ray Photoelectron Spectroscopy (XPS) and identified various surface coating compounds that can form during synthesis. Among the many common coating elements this study primarily focuses on tungsten (W). Using the in‐house “all dry synthesis” method, single crystal Li1+x(Ni0.6Mn0.3Co0.1)1‐xO2 materials are developed, where W is added at different stages of the synthesis to generate some unique W‐based surface compounds that match with vendor materials. This study systematically evaluates different W‐based surface compounds, compares their overall effect on electrochemical performance and highlights the instability of some of them. This work demonstrates that W is an important element and can significantly improve electrochemical performance when added in a second heating step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".