Investigating the phase diagram-ionic conductivity isotherm relationship in aqueous solutions of common acids: hydrochloric, nitric, sulfuric and phosphoric acid
Bibliographic record
Abstract
Abstract The relationship between phase diagram features around the solid–liquid equilibrium region and ionic conductivity in aqueous solutions is not well understood over the whole concentration range as is the case for acidic aqueous solutions. In this work, we have studied the ionic conductivity (κ) as a function of molar fraction (x) and temperature (T) for four acid/water solutions namely, monoprotic hydrochloric acid (HCl) and nitric acid (HNO3), diprotic sulfuric acid (H2SO4) and triprotic phosphoric acid (H3PO4) along with their binary phase diagrams. The connection between the main features of the phase diagrams and the trends in the ionic conductivity isotherms is established with a new insight on the two pertinent dominant conductivity mechanisms (hopping and vehicular). Ionic conductivity at different temperatures were collected from literature and fitted to reported isothermal (κ vs. x) and iso-compositional (κ vs. T) equations along with a novel semi-empirical equation (κ = f (x, T)) for diprotic and triprotic acids. This equation not only has the best fit for acids with different valency; but also contains four parameters, less than any other similar equation in literature. This work is one of few that advances the understanding of the intricate relationship between structure and ionic transport in various acidic aqueous solutions.
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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 | 0.000 |
| 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".