Thermodynamics Studies on the Solubility of Inorganic Salt in Organic Solvents: Application to KI in Organic Solvents and Water–Ethanol Mixtures
Bibliographic record
Abstract
A thermodynamic framework is established to calculate the solubility of inorganic salts in nonaqueous solvents. New solubility data of potassium iodide in selected organic solvents of acetone, ethanol, and 1-propanol are determined in the temperature range of 278–343 K. The experimental solubility is modeled by the methodology proposed in this work, and the solubility products of potassium iodide in organic solvents are estimated. Following this, the experimental mean activity coefficients are modeled with the electrolyte activity coefficient models of Pitzer, e -NRTL, e -Wilson equations, and their modified forms. Model parameters are optimized by fitting the experiment data. It turns out that the three-parameter e -Wilson equation presents the best correlation results with an overall average percentage relative deviation ARD of 1.36%. When the models are extrapolated to predict the low temperature solubility of potassium iodide in acetone down to 215 K, the three-parameter e -NRTL gives the most reliable predictions. A novel simple predictive mixing rule for the e -Wilson equation is proposed to estimate the solubility of potassium iodide in binary mixed solvents. The predictions are in good agreement with the experimental data of potassium iodide in water and ethanol over wide ranges of concentration and temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".