Understanding the Role of Prop-1-ene-1,3-Sultone and Vinylene Carbonate in LiNi<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>O<sub>2</sub>/Graphite Pouch Cells: Electrochemical, GC-MS and XPS Analysis
Bibliographic record
Abstract
The role of prop-1-ene-1,3-sultone (PES) used alone or in combination with vinylene carbonate (VC) in LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC)/graphite pouch cells was studied by correlating data from differential capacity (d Q /d V ) analysis, gas chromatography/mass spectroscopy (GC−MS), ultrahigh precision coulometry, storage experiments and X-ray photoelectron spectroscopy. VC formed more stable and protective SEI films at both graphite and NMC surfaces due to the formation of a polymer of VC. For PES-containing electrolytes, the preferential reaction of PES led to the formation of SEI films with higher oxygen content at both graphite and NMC surfaces due to the additional oxygen contribution of sulfite species and substantially less LiF compared to control and VC electrolytes. PES also led to thicker SEI films at the NMC surface. When VC was combined with PES, features of the SEI films from both VC and PES were observed. The SEI film features for VC and PES used alone or in combination can explain the improved electrochemical performance as well as the lower production of gas observed with these additives compared to control electrolyte.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".