Synthesis and Evaluation of Difluorophosphate Salt Electrolyte Additives for Lithium-Ion Batteries
Bibliographic record
Abstract
The electrolyte additive lithium difluorophosphate improves the lifetime of lithium-ion cells. This work presents the synthesis and evaluation of alternative difluorophosphate salt electrolyte additives. Ammonium difluorophosphate is readily prepared via a solid-state, benchtop reaction of ammonium fluoride and phosphorus pentoxide that requires only gentle heating to initiate. The best yield of sodium difluorophosphate (NaFO) in the present study was obtained by reacting difluorophosphoric acid and sodium carbonate in 1,2-diemethoxyethane over 3 Å molecular sieves. Tetramethylammonium difluorophosphate was prepared from NaFO via cation-exchange with tetramethylammonium chloride. NaFO is here reported to be a very good electrolyte additive, with similar performance in NMC532/graphite pouch cells as the lithium salt. The beneficial nature of both additives is attributable to the difluorophosphate anion. In contrast, ammonium and tetramethylammonium difluorophosphates are found to be poor electrolyte additives. For the former, this is suggested to be due to the formation of lithium nitride and hydrogen gas.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".