Structure-Dependent DC Conductivity and Relaxation Time in the Debye−Stokes−Einstein Equation
Bibliographic record
Abstract
The basis for a modification of the Debye-Stokes-Einstein (DSE) equation between the dc conductivity, sigma(dc), and dielectric relaxation time, tau, has been examined by using broad-band dielectric spectroscopy of LiClO4 solutions in 5-methyl-2-hexanol and 1-propanol and of pure liquids. According to the DSE equation, the log sigma(dc)-log tau plots should have a slope of -1. We find that sigma(dc) begins to depend upon the structure of an electrolytic solution when a variation of solvent's equilibrium dielectric permittivity, epsilon(s), with temperature causes the ion population to vary. As a consequence of this intrinsic dependence, the log sigma(dc)-log tau plots do not obey the DSE equation. Inclusion of the effect of change in epsilon(s) on the DSE equation may be useful in analyzing the measured quantities in terms of Brownian diffusion of both ions and molecules in ultraviscous liquids. Proton translocation along a hydrogen bond contributes little to sigma(dc), which appears to be predominantly determined by the ion population in the two alcohols and the solutions. The effect is briefly discussed in the potential energy landscape paradigm of structure fluctuations, and it is suggested that the high-frequency shear modulus measurements of ionic solutions would help reveal the temperature-dependent deviation from the DSE equation.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".