Quantitative Determination of Carbon Dioxide Content in Organic Electrolytes by Infrared Spectroscopy
Bibliographic record
Abstract
CO 2 has been shown to be an effective additive to improve the cycling characteristics of silicon negative electrodes for Li-ion batteries. However, a quantitative technique for measuring the CO 2 content in electrolyte is not readily available. Here, FTIR was used to accurately determine the CO 2 content of various carbonate-based Li-ion battery electrolytes. The accuracy of this method was validated with the weight variation method. Using this method, it was found that in EC/DEC electrolytes with 1M LiPF 6 and LiTFSI salts, CO 2 was found to have a maximum solubility of 0.37 wt% when the LiPF 6 :LiTFSI molar ratio is 3:1. In 1M LiPF 6 solutions, CO 2 solubility is in the order of PC ≈ EMC > DEC for the pure solvents. Mixed non-polar solvents are also favorable for CO 2 dissolution. When used in silicon alloy cells, electrolytes saturated with CO 2 (0.33% CO 2 in EC/DEC) resulted in the best cycling stability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".