Experimental Determination and Computational Prediction of Androstenedione Solubility in Alcohol + Water Mixtures
Bibliographic record
Abstract
This article evaluates the accuracy and applicability of three of the most common solubility models (i.e., Jouyban–Acree, NRTL-SAC, and COSMO-RS) in prediction of androstenedione (AD) solubility in binary mixtures of methanol + water and ethanol + water. The solubilities were measured from (275 to 325) K using medium-throughput experiments and then well represented mathematically by modified Apelblat and CNIBS/Redlich–Kister equations. The computational results show that AD solubility decreases monotonically with increasing water concentration in methanol + water mixtures, but it has a maximum at 0.15–0.30 mole fraction of water in the ethanol aqueous solution. Moreover, the performance of three solubility prediction models in this particular case was compared to identify the advantages and disadvantages of each model. The overall average relative deviation (ARD) for solubility prediction is 4.4% using Jouyban–Acree model, while it is 18.3% with NRTL-SAC model. Surprisingly, COSMO-RS model in combination with reference solubility achieves a good performance for solubility prediction in mixed solvents, including the prediction of synergistic effect of solvents, with overall ARD of only 4.9%.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".