Surprising Dependence of the Exfoliation of Graphite During Formation on Electrolyte Composition
Bibliographic record
Abstract
Graphite is the most used lithium intercalation host for the negative electrode of the lithium-ion battery. Extensive research has been carried out to achieve high Coulombic Efficiency (CE) and long cycle life for the graphite anode. Here, LFP/graphite (graphite from Vendor 1) cells that undergo formation at 40 °C with either 1.2 M LiPF6 dissolved in ethylene carbonate:dimethyl carbonate (EC:DMC), or ethylene carbonate:ethylmethyl carbonate (EC:EMC) have excellent first cycle efficiency (FCE). However, when the formation is done at 20 °C, EC:EMC and ethylene carbonate:diethyl carbonate (EC:DEC) cells show much reduced FCE while EC:DMC cells retain high FCE. We prove by a variety of experiments that the reduced FCE is caused by solvent co-intercalation. We explore the impact of temperature, different graphites, electrolyte additives, and varied salt content on this effect. We show that basic additives, such as vinylene carbonate, are sufficient to eliminate the co-intercalation. With a well-designed electrolyte system containing additives, graphites that show co-intercalation in the absence of additives perform equivalently or better than graphites that do not show co-intercalation in the absence of additives.
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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 | 0.001 |
| 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".