A Comparative Study of Pyridine Containing Lewis Acid-Base Adducts as Additives for Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/graphite Pouch Cells
Bibliographic record
Abstract
Three Lewis acid-base adducts including pyridine boron trifluoride (PBF), pyridine phosphorus pentafluoride (PPF) and pyridine sulfur trioxide (PSO) were used as electrolyte additives in Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite pouch cells (NMC532/graphite). PBF, PPF and PSO have a common Lewis base (pyridine) but have different Lewis acids. Experiments included ultra-high precision coulometry (UHPC), electrochemical impedance spectroscopy (EIS), gas evolution, long-term continuous charge-discharge cycling and charge-hold-discharge cycling. The results showed that cells containing PBF and PPF had the smallest voltage drop during storage at 4.5 V and at 60°C compared to cells with PSO and other additives such as triallyl phosphate (TAP) and vinylene carbonate (VC). UHPC results showed that the coulombic efficiency (CE) of the cells could be improved by using PBF or PPF either singly or in combination with other additives. The charge-hold-discharge cycling protocol can be used to distinguish the difference between additives relatively quickly compared to continuous cycling. Cells with 2% PBF had the best capacity retention compared to cells with the other additives at 40°C.
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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".