Dioxazolone and Nitrile Sulfite Electrolyte Additives for Lithium-Ion Cells
Bibliographic record
Abstract
Electrolyte additives are used in lithium-ion cells to achieve higher energy densities and longer lifetimes for electric vehicle and grid storage applications. This work tests a recently developed electrolyte additive, MDO (3-methyl-1,4,2-dioxazol-5-one) and introduces two new additives, PDO (3-phenyl-1,4,2-dioxazol-5-one) and BS (benzonitrile sulfite, 5-phenyl-1,3,2,4-dioxathiazole 2-oxide). The cell formation, high temperature storage and long-term cycling performance of lithium-ion NMC/graphite pouch cells prepared with these additives and binary blends thereof are presented. Differential capacity results indicate that MDO and PDO form passive solid-electrolyte interphase layers on the graphite electrode during cell formation, whereas BS does not. It is demonstrated that PDO is a highly promising new additive, especially when used as a binary blend with lithium difluorophosphate or ethylene sulfate. Future work is encouraged to explore the interactions between these additives and develop optimized solution chemistries, for example by adjusting the ratio of primary and secondary additives or through introducing ternary blends.
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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 | 0.000 |
| 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".