Optimization and modelling of pravastatin recovery from aqueous solutions using reactive extraction methodology
Bibliographic record
Abstract
Abstract This research endeavour primarily aimed to optimize and establish a model for the recovery of pravastatin, a well‐known statin drug recognized for its cholesterol‐lowering properties, from aqueous solutions using reactive extraction. This study used the Box–Behnken design to investigate extraction parameters and create a concise model equation illustrating the relationship between variables and responses. The investigation showed a strong agreement between the model equation and experimental outcomes. It highlighted a positive correlation, demonstrating that higher concentrations of reactant and pravastatin led to notable improvements in extraction efficiency. Two out of 90 experiments had extraction efficiencies below 10%, both with constant factors except for solvent type, while 14 experiments exceeding 80% efficiency highlighted the positive impact of a 50% reactant concentration on extraction efficiency. According to a statistical second‐order polynomial, the predicted extraction efficiency stands at 75.14%. In the culminating phase of the study, optimal extraction conditions were meticulously determined, resulting in the identification of an optimal set of parameters: a pravastatin concentration of 35 mg · L −1 , an adogen concentration of 20.91% (v/v), and a butanol phase ratio of 0.2% (v/v). Under optimal conditions, the experimental extraction efficiency reached 72.69%, closely aligning with values predicted through numerical optimization employing response surface methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".