Experimental Studies and Thermodynamic Modeling of the Solubilities of Potassium Nitrate, Potassium Chloride, Potassium Bromide, and Sodium Chloride in Dimethyl Sulfoxide
Bibliographic record
Abstract
Using appropriate nonaqueous solvents to replace water as reaction medium in chemical industries has gained more and more attention recently. Many of them have the special ability to dissolve some compounds and thus may make the reaction more stable in comparison with a water-containing environment. Dimethyl sulfoxide (DMSO) is probably the most frequently mentioned aprotic nonaqueous solvent with extensive applications because of its high stability and powerful solubility. In this study, the solubilities of four inorganic salts, namely potassium nitrate (KNO 3 ), potassium chloride (KCl), potassium bromide (KBr) and sodium chloride (NaCl), in DMSO are measured in the temperature range of 302 through 354 K using a dynamic method. The solubility order of the salts in DMSO is experimentally determined as KNO 3 > KBr > NaCl > KCl. The molality solubilities show linear dependencies on temperature and the temperature effect on the solubility for the salts follows the same order as the solubility result. The solubility products of the salts in DMSO at different temperatures are obtained by estimating the solubility products in water and the Gibbs energy of transfer from water to DMSO. Then electrolyte models of the Wilson, NRTL, and UNIQUAC equations are used to model the solubility of the inorganic salts in DMSO. It is found that the three-parameter E-Wilson equation gives the best correlation results followed by the Pitzer, E-NRTL, and E-UNIQUAC equations, while the two-parameter E-Wilson equation presents the worst results in terms of the overall standard deviation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".