A Consideration of Electrolyte Additives for LiNi<sub>0.5</sub>Mn<sub>1.5</sub>O<sub>4</sub>/Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub>Li-Ion Cells
Bibliographic record
Abstract
LNMO/LTO cells represent an excellent vehicle with which to test the impact of electrolyte additives on cell performance due to the high potential of LNMO which leads to electrolyte oxidation. Additives that prevent this oxidation are desired. Electrolyte additive researchers normally compare the capacity retention versus cycle number of cells with and without additives. If the capacity retention is improved, the additive is "good". In this paper, this logic is shown to be flawed. One additive, LiO- t -C 4 F 9 , which does improve capacity retention versus cycle number, is shown to do so by increasing electrolyte oxidation at the positive electrode. This causes increased charge end point capacity slippage and increased self discharge rates. As such, these additives, like many promoted in the literature, are not "good", but are "bad". On the other hand, one additive, Al(HFiP) 3 , is shown to reduce parasitic reactions at the LNMO electrode but does not lead to improved capacity retention. A full understanding of the impact of the additive on the parasitic reaction rates at both positive and negative electrodes is required before a sound judgment about the impact of an additive can be made. Therefore, additive researchers must pay attention to coulombic efficiency, capacity end point slippage, reversible and irreversible capacity loss during storage as well as voltage drop during storage in addition to the long-time cycling performance for cells to get an overall evaluation of electrolyte additives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".