Insights into the translational and rotational dynamics of cations and anions in protic ionic liquids by means of NMR fast-field-cycling relaxometry
Bibliographic record
Abstract
Understanding the translational and rotational dynamics of cations and anions in hydrogen bonded protic ionic liquids (PIls) is still a challenge. In this study, we determine self-diffusion coefficients and rotational correlation times of both ions in triethylammonium based PILs by means of NMR Fast-Field-Cycling (FFC) relaxometry. Global fits of 1H and 19F nuclear magnetic relaxation dispersion (NMRD) curves allowed proper separation into intra and inter molecular relaxation rates for both NMR sensitive nuclei and thus a reliable description of translational and rotational motion for both ions individually. The diffusion coefficients of the cations are in the order of 6 × 10-11 m2 s-1 at room temperature and about 50 per cent larger than those of the anions. The diffusion coefficients of cations and anions in both PILs were compared with those we derived from applying an universal dispersion power law and those known from pulsed field gradient (PFG) NMR studies. Considering the Nernst-Einstein equation, molar conductivities were calculated from cationic and anionic diffusion coefficients and related to directly measured molar conductivities, allowing the determination of the degree of dissociation. The rotational correlation times τR ranging from 50 ps up to 2 ns as a function of temperature were compared with those obtained from high-field NMR quadrupolar relaxation time measurements addressing explicitly the rotation of the NH vector and giving insights into the acidic proton mobility. The Stokes-Einstein and Stokes-Einstein-Debye relations were applied to relate the diffusion coefficients and rotational correlation times to the macroscopic bulk viscosity. The results were also discussed with respect to the archetypical PIL ethylammonium nitrate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".