Molecular Dynamic Simulation of Dicationic Ionic Liquids: Effects of Anions and Alkyl Chain Length on Liquid Structure and Diffusion
Bibliographic record
Abstract
Structures and dynamics of nine geminal dicationic ionic liquids (DILs) Cn(mim)2X2, where n = 3, 6, and 9 and X = PF6(-), BF4(-), and Br(-), were studied by molecular dynamic simulations (J. Phys. Chem.B2004, 108, 2038-2047). A force field with a minor modification for C3(mim)2 × 2 was adopted for the simulations. Densities, detailed microscopic structures, mean-square displacements (MSD), and self-diffusivities for various ion pairs from MD simulations have been presented. The calculated densities for C9(mim)2X2 (X = Br(-) and BF4(-)) agreed well with the experimental values. The calculated RDFs show that anions are well organized around the imidazolium rings. The calculated RDFs indicate that, unlike the mono cationic ILs, the anions and cations in DILs distribute homogeneously. Enthalpies of vaporization were calculated and correlated with the structural features of DILs. The local structure of C9(mim)2X2 (X = Br, PF6) was examined by the spatial distribution function (SDF). The calculated SDFs show that similar trends were found by other groups for mono cationic ionic liquids (ILs). The highest probability densities are located around the imidazolium ring hydrogens. The calculated diffusion coefficients show that the ion diffusivities are 1 order of magnitude smaller than that of the mono cationic ionic liquids. The effects of alkyl chain length and anion type on the diffusion coefficient were also studied. The dynamics of the imidazolium rings and the alkyl chain in different time scales have also discussed. The calculated transference numbers show that the anions have the major role in carrying the electric current in a DIL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".