Cycling Efficiency Improvement of Solid-State Batteries-Laser Textured Interfaces
Bibliographic record
Abstract
Solid-state lithium metal batteries (SS-LMBs) with ceramic electrolytes have attracted significant interest towards their potential to produce safe and powerful storage devices. Among ceramic electrolyte materials utilized in SS-LMBs, non-flammable perovskites (La2/3-xLi3xTiO3, LLTO) are cost-effective and have a wider electrochemical window than conventional liquid electrolytes (> 5V). However, the high interfacial resistance at the LLTO/cathode interface restricts the reversible capacity and the cycling efficiency of SSLMBs. Herein, a novel approach of laser texturing at the interface was investigated as a solution to these two challenges. In this study, the laser-texturing technique was first used to produce micro-patterns on the side of the LLTO against the cathode. This increased surface contact area provided more pathways for Li-ion migration at the interface, leading to decreased interfacial resistance and an improved battery energy density. A few drops of liquid electrolyte were added to fill the void spaces between the laser-textured LLTO and the cathode. Overall, this study conceptually proved the effectiveness of using laser texturing to improve the interfacial contact. Further efforts are needed to optimize the pattern design and establish a more stable interface that could enable long-term cycling of the SS-LMBs.
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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".