Optimal Solvent Screening for the Crystallization of Pharmaceutical Compounds from Multisolvent Systems
Bibliographic record
Abstract
In this study, an effort has been made to predict the solid–liquid equilibrium (SLE) behavior of different solids (pharmaceuticals) in many common solvents and their mixtures. A modified optimization of a recent thermodynamic model, the NRTL–SAC model, was used in all stages of calculation (VLE, LLE, and SLE predictions). The batch cooling–antisolvent crystallization process was simulated for seven model molecules from the initial temperature to the final temperature and for the volume fraction of each solvent. The feasible region of temperature for each crystallization case was calculated based on the bubble-point temperature of the solvent mixture and the melting point of the model molecules. The NRTL–SAC model was used in conjunction with the optimization procedure to test the complete miscibility of solvents during each part of crystallization. After estimating the optimum solvent mixture (combination) for a specific model molecule, the results for single, binary, and ternary solvent mixtures were compared. The results obtained from the binary and ternary combinations were similar in terms of crystallization yields per mass of solvent mixture and far superior to those obtained with single solvents. The proposed algorithm demonstrates flexibility, simplicity, and accuracy in predicting the phase behavior and eventual optimal solvent screening for the crystallization of pharmaceutical components.
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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 | 0.001 | 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".