Precision Measurements of the Coulombic Efficiency of Lithium-Ion Batteries and of Electrode Materials for Lithium-Ion Batteries
Bibliographic record
Abstract
Undesired reactions in Li-ion batteries, which lead to capacity loss, can consume or produce charge at either the positive or negative electrode. For example, the formation and repair of the solid electrolyte interphase consumes and at the negative electrode. High purity electrolytes, elimination of water, various electrolyte additives, electrode coatings, and special electrode materials are known to improve cycle life and therefore must impact coulombic efficiency. Careful measurements of coulombic efficiency are needed to quantify the impact of trace impurities, additives, coatings, etc., in only a few charge–discharge cycles and in a relatively short time. The effects of cycle-induced and time-related capacity loss could be probed by using experiments carried out at different C-rates. In order to make an impact on Li-ion cells for automotive and energy storage applications, where thousands of charge–discharge cycles are required, coulombic efficiency must be measured on the order of 0.01%. In this paper, we describe an instrument designed to make high precision coulombic efficiency measurements and give examples of its use on commercial Li-ion cells and Li half-cells. High precision coulombic efficiency measurements can detect problems occurring in half-cells that do not lead to capacity loss, but would in full cells, and can measure the impact of electrolyte additives and electrode coatings.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".