Electrolyte Formulations Based on Dinitrile Solvents for High Voltage Li-Ion Batteries
Bibliographic record
Abstract
In this work, we have investigated the suitability of aliphatic dinitrile solvents with the chemical formula N≡C-(CH 2 ) n -C≡N where n varies from 3 to 8 in single, binary (mixed with ethylene carbonate, EC) or ternary (mixed with EC and dimethyl carbonate, DMC) electrolyte solutions for the high voltage (4.7 V) LiMn 1.5 Ni 0.5 O 4 cathode material in lithium batteries. We report that the conductivity of all the electrolyte solutions (with LiTFSI or LiBF 4 as salt) decreases as a function of “ n ”, i.e. as the alkane chain become longer while the viscosity increases. The electrochemical stability window is about 7 V for the single electrolyte solutions and drops to 6–6.5 V for the binary and ternary ones. ATR IR spectra of all the electrolyte solutions indicate the presence of a strong interaction between Li ions and the different solvents. Li/LiMn 1.5 Ni 0.5 O 4 half cell batteries assembled using dinitriles as the main solvent (50% by volume), LiBF 4 salt and LiBOB co-salt show good performance only in the ternary solutions. Those with shorter alkane dinitriles with n = 4 and 5 retain the capacity better after 50 cycles than the longer ones with n = 6 and 8. Investigation of the surface of the cycled electrode by XPS reveals that DMC plays a great role in surface passivation at high voltages by preventing salt decomposition in ternary solutions.
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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".