Molecular dynamics simulation of imidazolium-based ionic liquids. II. Transport coefficients
Bibliographic record
Abstract
A systematic molecular dynamics study is performed to determine the dynamics and transport properties of 12 room-temperature ionic liquids family with 1-alkyl-3-methylimidazolium cation, [amim](+) (alkyl = methyl, ethyl, propyl, and butyl), with counterions, PF(6)(-), NO(3)(-), and Cl(-). The goal of the work is to provide molecular level understanding of the transport coefficients of these liquids as guidance to experimentalists on choosing anion and cation pairs to match required properties of ionic liquid solvents. In the earlier paper (Part I), we characterized the dynamics of ionic liquids and provided a detailed comparison of the diffusion coefficients for each ion using the Einstein and Green-Kubo formulas. In this second part, other transport properties of imidazolium salts are calculated, in particular, the electrical conductivity is calculated from the Nernst-Einstein and Green-Kubo formulas. The viscosity is also determined from the Stokes-Einstein relation. The results of the calculated transport coefficients are consistent with the previous computational and experimental studies of imidazolium salts. Generally, the simulations give electrical conductivity lower than experiment while the viscosity estimate is higher than experiment. Within the same cation family, the ionic liquids with the NO(3)(-) counterion have the highest electrical conductivities: sigma[NO(3)](-)>sigma[PF(6)](-)>sigma[Cl](-). The [dmim][X] series, due to their symmetric cationic structure and good packing and the [bmim][X] series due to higher inductive van der Waals interactions of [bmim](+), have the highest viscosities in these ionic liquid series. Our simulations show that the major factors determining the magnitude of the self-diffusion, electrical conductivity, and viscosity are the geometric shape, ion size, and the delocalization of the ionic charge in the anion.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".