Conductivity Maxima Vs. Temperature: Grotthuss Conductivity in Aprotic Molten Salts<sub/>
Bibliographic record
Abstract
The phenomenon of electrical conductivity maxima of molten salts versus temperature during orthobaric (closed-vessel) conditions is further examined. Previously, in a study of molten BiCl3, a new theory was offered in which the conductivity falloff at high temperatures is due not to a shift in equilibrium, but to a rise in the barrier for atomic ions hopping from counterion to counterion. There, conductivity was presented as an ordinary Arrhenius equation in which the frequency prefactor A (for hopping opportunities) and activation energy Ea (for hopping) are density dependent. Here we further test this idea on another high-conducting melt (SnCl2, σmax = 2.81 Ω−1 cm−1) and a low-conducting one (HgBr2, σmax = 4.06 x 10-4 Ω −1 cm−1). First, ab initio molecular dynamics simulations were performed and successfully reproduced the maxima for both these liquids. Second, use of two different ab initio definitions of the Arrhenius prefactor lead to reasonable activation energies for hopping. In the HgBr2 case, a very molecular liquid like water, a clear Grotthuss chain of bromide transfers was observed in simulation when seeding the system with a HgBr+ cation and HgBr3 − anion. The main conclusion is that the Grotthuss mechanism for conduction, currently ignored for all but H+ ions, also occurs with halide ions in “covalent” metal halide melts (involving electronegative metals), and this mechanism is the cause of the conductivity maximum.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".