In Situ Coating Li<sub>3</sub>PO<sub>4</sub> on Li<sub>6.5</sub>La<sub>3</sub>Zr<sub>1.5</sub>Ta<sub>0.5</sub>O<sub>12</sub> Achieving Lithium Dendrites Inhibition and High Chemical Stability
Bibliographic record
Abstract
ABSTRACT Solid‐state electrolyte (SSE) is a potential way to solve the safety problems of lithium metal batteries (LMBs), and Li 6.5 La 3 Zr 1.5 Ta 0.5 O 12 (LLZTO) is one of the most extensive research SSEs due to its good air stability and wide electrochemical window. However, the residual alkali on LLZTO surface limits its application with polyvinylidene difluoride (PVDF)‐contained binders, and the uncontrollable lithium dendrites growing between the grain boundaries of LLZTO particles would lead to rapid capacity fading and potential short circuit risk. Herein, by in situ coating Li 3 PO 4 (LPO) on LLZTO particles (LLZTO@LPO) evenly, the residual alkali on the LLZTO surface is neutralized and the pH value is reduced to 8.84. The modified LLZTO can be mixed with PVDF solution and shows good fluidity without a cross‐linking reaction, making the subsequent ceramic coating on the separator feasible. The LLZTO@LPO coating polyethylene (PE) separator can achieve 1400 h (115% increase) stable cycling under 1 mA cm −2 current density in the Li∥Li symmetrical cell and 80% capacity retention after 260 cycles (NCM622‐Li coin cell with 3 mAh cm −2 loading). Furthermore, the LLZTO SSE pellets were prepared with the LLZTO@LPO and assembled in coin cell. The critical current density (CCD) result increases from 0.7 to 1.6 mA cm −2 owing to that the LPO coating effectively inhibits the lithium dendrites formation through LLZTO grain boundaries. This work provides a strategy for fabricating the coating layer on LLZTO to improve the stability of LMBs.
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) | 0.002 | 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".