Development of Pyridine-Boron Trifluoride Electrolyte Additives for Lithium-Ion Batteries
Bibliographic record
Abstract
A series of novel electrolyte additives based on Lewis acid/base adducts has been designed and successfully synthesized. The synthesis is very simple: a pyridine derivative is mixed with boron trifluoride dissolved in diethyl ether to yield a solid crystalline product. The effect of Pyridine-Boron Trifluoride (PBF) and its derivatives have been thoroughly evaluated in Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 /graphite and Li[Ni 0.42 Mn 0.42 Co 0.16 ]O 2 /graphite pouch cells. Evaluation experiments, including high voltage storage, gas production, electrochemical impedance spectroscopy measurements, ultra high precision cycling and long-term cycling were carried out on cells containing the novel additives. The results were compared to baseline experiments on cells with well-known additives such as vinylene carbonate (VC), prop-1-ene sultone (PES), methylene methane disulfonate (MMDS), tris(-trimethyl-silyl)-phosphite (TTSPi) and triallyl phosphate (TAP). The PBF additives are competitive with all known additives in NMC/graphite cells, which, combined with their low cost and facile synthesis, suggest this series of novel additives will be useful in high-voltage/high-temperature lithium ion battery applications. The PBF additives yield cells which show excellent capacity retention and maintain low impedance during high voltage cycling, in contrast to cells containing VC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".