Surface‐to‐Bulk Synergistic Modification of Single Crystal Cathode Enables Stable Cycling of Sulfide‐Based All‐Solid‐State Batteries at 4.4 V
Bibliographic record
Abstract
Abstract The interfacial stability between sulfide solid‐state electrolytes (SSEs) and high voltage Ni‐rich oxide cathodes is critical to the electrochemical performances of all‐solid‐state batteries (ASSBs), yet it is challenging to solve the interface issues by surface coating modification. Here, a surface‐to‐bulk synergistic modification is proposed to achieve a highly stable interface through the combination of TiNb 2 O 7 ‐coated and Ti‐doped LiNi 0.6 Mn 0.2 Co 0.2 O 2 single crystals (DC‐TNO@SCNCM). The TiNb 2 O 7 coating layer with thermodynamic/electrochemical stability and electronic insulation avoids the decomposition of SSEs. The strong TiO bond in SCNCM achieved by Ti doping can stabilize lattice oxygen and avoid further electrochemically oxidizing sulfide electrolytes to form oxygenated sulfurous and phosphorous species. The modified DC‐TNO@SCNCM cathode exhibits excellent long‐cycle stability with a capacity retention rate of 92.2% after 140 cycles at a high cut‐off voltage of 4.4 V. This surface‐to‐bulk synergistic modification strategy provides a new perspective for the design of high‐voltage sulfide‐based ASSBs.
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".