Computational and experimental characterization of a pyrrolidinium-based ionic liquid for electrolyte applications
Bibliographic record
Abstract
The development of Li-ion batteries for energy storage has received significant attention. The synthesis and characterization of electrolytes in these batteries are an important component of this development. Ionic liquids (ILs) have been proposed as possible electrolytes in these devices. Thus, the accurate determination of thermophysical properties for these solvents becomes important for determining their applicability as electrolytes. In this contribution, we present the synthesis and experimental/computational characterization of thermodynamic and transport properties of a pyrrolidinium based ionic liquid as a first step to investigate the possible applicability of this class of ILs for Li-ion batteries. A quantum mechanical-based force field with many-body polarizable interactions has been developed for the simulation of spirocyclic pyrrolidinium, [sPyr+], with BF4− and Li+. Molecular dynamics calculations employing intra-molecular polarization predicted larger heat of vaporization and self-diffusion coefficients and smaller densities in comparison with the model without intra-molecular polarization, indicating that the inclusion of this term can significantly effect the inter-ionic interactions. The calculated properties are in good agreement with available experimental data for similar IL pairs and isothermal titration calorimetry data for [sPyr+][BF4−].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".