Combinatorial Methods for Improving Lithium Metal Cycling Efficiency
Bibliographic record
Abstract
An efficient combinatorial approach for the design and evaluation of surface coatings to improve lithium metal cycling efficiency is demonstrated. The reliability of a 64 electrode combinatorial cell was verified through lithium metal cycling in a range of electrolytes on unmodified Ni and Cu electrode plates with parallel verification using conventional Cu|| Li coin cells. A 1M LiPF 6 FEC:TFEC fluorinated electrolyte demonstrated the best performance with coulombic efficiencies of 98.3% and 97.6% in the combinatorial cell (cyclic voltammetry) and coin cell (galvanostatic cycling) respectively, compared to only 91.0% (CV) and 90.3% (galvanostatic) for a 1M LiPF 6 EC:DEC control electrolyte. High throughput sputtering was used to deposit different thin film coatings of varying thickness on the 64-channel cell plates to probe the lithium cycling performance as a function of electrolyte and surface coating. Zn surface coatings with thickness between 400–900 nm proved most beneficial; significantly reducing the lithium nucleation potential and leading to more stable cycling at high coulombic efficiency (99.0% in the combinatorial cell with FEC:TFEC electrolyte). These findings not only highlight some useful strategies for improving lithium metal cycling efficiency, but also show the potential for more advanced combinatorial analysis to design superior anodes for lithium metal cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".