Solubility prediction of anthracene in nonaqueous solvent mixtures using a combination of Jouyban-Acree and Abraham models
Bibliographic record
Abstract
The applicability of previously developed quantitative structure-property relationships was extended to predict the solubility of anthracene in nonaqueous binary and ternary solvent mixtures. The accuracy of the proposed methods was evaluated using 81 solubility data sets collected from the literature. The individual and mean percentage deviation (IPD and MPD) of experimental and computed solubilities were calculated as accuracy criteria. The computations were carried out using experimental and predicted mole fraction solubility of anthracene in monosolvent systems for binary and ternary solvent systems. The overall MPD of solubility prediction using experimental values in monosolvents varied from 5.2% to 4.2% and from 16.5% to 10.7% for binary and ternary solvents, using water to solvent and gas to solvent solvational parameters, respectively. The IPD distribution was better for the gas to solvent model. The corresponding ranges for the predicted solubility of anthracene in monosolvents were 47.9% to 28.1% and 23.9% to 22.5% for binary and ternary solvents, respectively, and IPD distribution was more favourable for the gas to solvent model. In general, the models derived from gas to solvent coefficients provided more accurate predictions and are recommended for practical applications.Key words: solubility, prediction, cosolvency, anthracene, Abraham model, Jouyban-Acree model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".