In Situ Investigations of SEI Layer Growth on Electrode Materials for Lithium-Ion Batteries Using Spectroscopic Ellipsometry
Bibliographic record
Abstract
Thin film electrodes of a-Si and TiN were prepared by magnetron sputtering for use with a custom-designed tubular in-situ spectroscopic ellipsometry (SE) electrochemical cell. Ellipsometric measurements on Li/a-Si in-situ cells with 0.1 M LiPF 6 /EC:DEC (1:2) electrolyte were made while cells were charged and discharged. Large reversible changes in the ellipsometric parameters, Ψ and Δ, were closely related to the state of charge of the in-situ cell. The transformation of semiconducting a-Si to metallic a-Li x Si alloy and the formation of an SEI layer contributed to these large changes, which we were unable to decouple. Li/TiN cells were made so that the SEI forming process could be studied without competing reactions. The effect of vinylene carbonate (VC) and fluoroethylene carbonate (FEC) additives on SEI formation were studied. SEI thicknesses for cells containing no additives, VC, and FEC were roughly 18 nm, 25 nm and 30 nm, respectively, after a 10 h hold at 0.1 V vs Li/Li + .
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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) | 0.000 | 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) | 0.001 | 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".