Optimizing Electrolyte Additive Loadings in NMC532/Graphite Cells: Vinylene Carbonate and Ethylene Sulfate
Bibliographic record
Abstract
A matrix of LiNi0.5Mn0.3Co0.2O2/graphite cells filled with 1.33 molal LiPF6 in EC:EMC:DMC (ethylene carbonate: ethyl methyl carbonate: dimethyl carbonate) (25:5:70 by volume) electrolyte and different weight percentages of vinylene carbonate (VC) and ethylene sulfate (DTD) electrolyte additives underwent prolonged charge-discharge cycling at 20 °C and 40 °C. The volume of gas produced during formation and cycle testing was measured. The impedance spectra of the cells before and after cycling was measured. After testing, the electrolyte was extracted for study by nuclear magnetic resonance spectroscopy (NMR) and gas chromatography/mass spectroscopy (GC-MS) to determine what changes in electrolyte composition had occurred. Some cells had their negative electrodes studied by scanning micro-X-ray fluorescence to quantify the amount of transition metals that transferred from the positive electrode to the negative electrode during the testing. Cells containing 1% VC or 2% VC with an additional 1% DTD by weight had the best capacity retention and lowest impedance growth. NMR and GC-MS suggest that these additive combinations promote increased electrolyte salt consumption which may represent a source of lithium to replenish the lithium inventory. Only a small amount of transition metals (0.03% or less) originating from the positive electrode active material was found on the negative electrode after testing. Most cells had over 1500 cycles at both 20 °C and 40 °C conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".