Concentration Dependent Solution Structure and Transport Mechanism in High Voltage LiTFSI–Adiponitrile Electrolytes
Bibliographic record
Abstract
The physiochemical properties of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in adiponitrile (ADN) electrolytes were explored as a function of concentration. The phase diagram and ionic conductivity plots show a distinct relationship between the eutectic composition of the electrolyte and the concentration of maximum ionic conductivity in the 25 °C isotherm. We propose a structure-based explanation for the variation of electrolyte ionic conductivity with LiTFSI concentration, where the eutectic concentration is a transitionary region at which the structure changes from solvated contact ion pairs to extended units of [Li z (ADN) x TFSI y ] z−y aggregates. It is found through diffusion coefficient measurements using pulsed-field gradient (PFG) NMR that both D L i / D T F S I and D L i / D A D N increase with concentration until 2.9 M, where after Li + becomes the fastest diffusing species, suggesting that ion hopping becomes the dominant transport mechanism for Li + . Variable diffusion-time (Δ) PFG NMR is used to track this evolution of the ion transport mechanism. A differentiation in Li + transport between the micro and bulk levels that increases with concentration was observed. It is proposed that ion hopping within [Li z (ADN) x TFSI y ] z−y aggregates dominates the micro-scale, while the bulk-scale is governed by vehicular transport. Lastly, we demonstrate that LiTFSI in ADN is a suitable electrolyte system for use in Li-O 2 cells.
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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".