An Artificial Lithium Protective Layer that Enables the Use of Acetonitrile‐Based Electrolytes in Lithium Metal Batteries
Bibliographic record
Abstract
Abstract The resurgence of the lithium metal battery requires innovations in technology, including the use of non‐conventional liquid electrolytes. The inherent electrochemical potential of lithium metal (−3.04 V vs. SHE) inevitably limits its use in many solvents, such as acetonitrile, which could provide electrolytes with increased conductivity. The aim of this work is to produce an artificial passivation layer at the lithium metal/electrolyte interface that is electrochemically stable in acetonitrile‐based electrolytes. To produce such a stable interface, the lithium metal was immersed in fluoroethylene carbonate (FEC) to generate a passivation layer via the spontaneous decomposition of the solvent. With this passivation layer, the chemical stability of lithium metal is shown for the first time in 1 m LiPF6 in acetonitrile.
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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) | 0.000 | 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".