Optimizing Electrolyte Formulations for Better Lithium-Ion Batteries
Bibliographic record
Abstract
In this presentation different electrolyte formulations that can improve the performance of current Li-ion batteries will be discussed. One is based on the idea of mixing ionic liquids with carbonate solvents in order to combine the advantages of the two and achieve thermally stable and nonflammable electrolyte mixtures with good cycling performance. Another is based on aliphatic dinitriles solvents, NC-(CH2)n-CN, n = 3-8, that show good thermal and electrochemical stability (6-8 V). The variation in physical properties, crystallographic structure and battery performance of the different dinitrile-based electrolytes will be discussed. Also, the use of these electrolyte formulations for high voltage batteries with the 4.7 V cathode material LiNi0.5Mn1.5O4 will be discussed in details. [1] Y. Abu-Lebdeh and I. Davidson, J. Electrochem. Soc. 2009, 156 A60; J. Power Sources 2009, 189 576 [2] H. Duncan, N. Salem, Y. Abu-Lebdeh, J. Electrochem. Soc.2013, 160 A838
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".