Artificial Cathode-Electrolyte Interphase towards High-Performance Lithium-Ion Batteries: A Case Study of β-AgVO3
Bibliographic record
Abstract
Silver vanadates (SVOs) have been widely investigated as cathode materials for high-performance lithium-ion batteries (LIBs). However, similar to most vanadium-based materials, SVOs suffer from structural collapse/amorphization and vanadium dissolution from the electrode into the electrolyte during the Li insertion and extraction process, causing poor electrochemical performance in LIBs. We employ ultrathin Al2O3 coatings to modify β-AgVO3 (as a typical example of SVOs) by an atomic layer deposition (ALD) technique. The galvanostatic charge-discharge test reveals that ALD Al2O3 coatings with different thicknesses greatly affected the cycling performance. Especially, the β-AgVO3 electrode with ~10 nm Al2O3 coating (100 ALD cycles) exhibits a high specific capacity of 271 mAh g−1, and capacity retention is 31%, much higher than the uncoated one of 10% after 100 cycles. The Coulombic efficiency is improved from 89.8% for the pristine β-AgVO3 to 98.2% for Al2O3-coated one. Postcycling analysis by cyclic voltammetry (CV), cyclic voltammetry (EIS), and scanning electron microscopy (SEM) disclose that 10-nm Al2O3 coating greatly reduces cathode-electrolyte interphase (CEI) resistance and the charge transfer resistance in the β-AgVO3 electrode. Al2O3 coating by the ALD method is a promising technique to construct artificial CEI and stabilize the structure of SVOs, providing new insights for vanadium-based electrodes and their energy storage devices.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".