The origin of the conductivity maximum vs. mixing ratio in pyridine/acetic acid and water/acetic acid
Bibliographic record
Abstract
Explanations are provided for the first time for the historically known locations of electrical conductivity maxima versus mixing ratio (mole fraction of acid, xA) in mixtures of (i) acetic acid with water and (ii) acetic acid with pyridine. To resolve the question for the second system, density-functional-based molecular dynamic simulations were performed, at 1:1, 1:2, 1:3, 1:5, and 1:15 mixing ratios, to gain vital information about speciation. In a zeroth-order picture, the degree of ionization (and hence conductivity) would be maximal at xA = 0.5, but these two examples see this maximum shifted to the left (water/acetic acid, xAmax = 0.06), due to improved ion stability when the effective dielectric constant is high (i.e., water-rich mixtures), or right (pyridine/acetic acid xAmax = 0.83), due to improved acetate stability via “self-solvation” with acetic acid molecules (i.e., acid-rich mixtures) when the dielectric constant is low. A two-parameter equation, with theoretical justification, is shown to reproduce the entire 0 < xA < 1 range of data for electrical conductivity for both systems. Future work will pursue the applicability of these equations to other amine/carboxylic acid mixtures; preliminary fits to a third system (trimethylamine/acetic acid) give curious parameter values.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".