Prediction of Solubilities of Solid Biomolecules in Modified Supercritical Fluids Using Group Contribution Methods and Equations of State
Bibliographic record
Abstract
A method to predict the solubilities of biomolecules in supercritical fluids (SCF) with cosolvents was developed, and predictions were compared with experimental data available in the literature. The method used the Constantinou−Gani and Joback group contribution methods to estimate pure component solute properties and the Lee−Kesler−Plöcker (LKP) and Mohsen-Nia−Moddaress−Mansoori (MMM) equations of state (EOS) to determine solute solubilities in the ternary solute−SCF−cosolvent systems. The ternary systems were modeled as pseudobinary systems with the SCF−cosolvent binary pair being modeled as a single pseudocomponent using Kay’s mixing rule to estimate the pseudocomponent properties. Twenty-one (21) systems consisting of polar and nonpolar solutes and cosolvents were evaluated over a range of temperatures, pressures, and cosolvent concentrations. The results demonstrated that both the LKP and MMM EOS are useful for modeling the solubility of solids in supercritical fluids (SCF) with cosolvents. When the Aromaticity index (AI) was less than or equal to 0.3, the MMM EOS was found to be more suitable; otherwise the LKP EOS was found to provide the best fit. If this so-called AI criterion was used, the average error between predicted and experimental results ranged from 0.5 to 25.5% with an average of 10.0% for the 21 systems studied. However, when the AI criterion was not followed, the error between predicted and experimental results ranged from 9.6 to 99% with an average of 69.0%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".