Effects of Electrolyte Additives and Solvents on Unwanted Lithium Plating in Lithium-Ion Cells
Bibliographic record
Abstract
Unwanted lithium plating on the graphite anode of lithium ion batteries can reduce the cycle life and safety of lithium ion batteries. Increased charging rates, lower temperatures, thicker electrodes, lower Li-ion diffusion constant and larger graphite particles all increase the propensity for unwanted lithium plating. In this work, a variety of electrolyte additives and electrolytes, which extend lifetime during low rate cycling, were used in Li[Ni1/3Mn1/3Co1/3]O2/graphite (NMC111/graphite) pouch cells subjected to high rate charging at 20°C. It was found that additives and electrolytes which increased the negative electrode area-specific resistance, Rnegative, decreased the onset current, Iu, for unwanted lithium plating. Here, the processes of ion desolvation, electron and ion transport through the solid electrolyte interphase and contact resistance are lumped into the Rnegative. Under conditions where Rnegative is the dominant factor determining when unwanted Li plating occurs, the onset current for lithium plating could be well predicted by the expression: Iu = 0.080 V x S/Rnegative, where S is the geometric electrode surface area. Rnegative is easily determined using negative electrode coin-type symmetric cells. This simple rule-of-thumb relation will help guide researchers seeking to select electrolyte additives that simultaneously increase lifetime and also allow fast charging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".