Modeling Solubility of Polycyclic Aromatic Compounds in Subcritical Water
Bibliographic record
Abstract
Polycyclic aromatic compounds (PACs) are common environmental contaminants associated with oil spills and the incomplete combustion of organic materials. PAC solubility in water is a fundamental property for environmental studies, and modeling of these data improve the process of environmental risk assessment. In this study, seven models (UNIQUAC, local surface Guggenheim, NRTL, regular solution, Wilson, Van Laar, and a modified Van Laar model) for correlations and prediction of aqueous solubilities of 22 PACs were evaluated. The results using models based on Guggenheim’s method showed that the local surface Guggenheim model provided a better correlation than the UNIQUAC model. For the systems studied, the best correlations were obtained with NRTL, Van Laar, and modified Van Laar models with mean deviations of 17.1, 14.3, and 14.5%, respectively. The predicted solubilities using NRTL and modified Van Laar models provided mean deviations of 44.1 and 47.1%, respectively. The sensitivity analysis showed that the correlations using the NRTL model are slightly influenced by variations up to 20% of the triple-point temperature and molar enthalpy of fusion of the solute.
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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 | 0.001 | 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 | 0.001 |
| 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 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".