Identifying the Polymorphic Outcome of Hypothetical Polymorphs in Batch and Continuous Crystallizers by Numerical Simulation
Bibliographic record
Abstract
Polymorphism is one of the most important challenges in pharmaceutical manufacturing. However, the strategy to crystallize the desired polymorph has not been extensively investigated, especially in continuous crystallization. In this work, a numerical model, incorporating the population balance modeling, was developed considering the nucleation and growth rates of metastable and stable forms of a number of pharmaceutical solids. The impact of relative nucleation and growth kinetics of the two polymorphs on the polymorphic outcome was studied in batch and MSMPR (mixed suspension and mixed product removal) crystallizers. In both modes of operation, the simulation results show that the growth rate has a more significant effect than the birth rate. In batch crystallizers, an indicator has been proposed to analyze the time window to remove the metastable form. In MSMPR crystallizer, this indicator can be used to check whether the operating conditions (crystallizer temperature, residence time, and inlet concentration) can alter the steady-state polymorph. It is found that at high crystallizer temperature, low inlet concentration, and long residence time, the production of the stable form is favored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".