Ultrasound‐assisted cooling crystallization of edaravone for improving the crystal characteristics
Bibliographic record
Abstract
Abstract The crystallization of edarvone, an active pharmaceutical ingredient (API), was improved utilizing an ultrasound reactor focusing on understanding the effect of different operating parameters on the yield, particle size distribution, and shape of crystals. The best operating conditions for maximum intensification benefits of using ultrasound were elucidated as 100 W ultrasonic power, 60% duty cycle, and 35 min as sonication time. Use of ultrasound resulted in significant increase in the yield to 81.34% compared to only 35% yield using conventional reactor, under otherwise similar conditions. Particle size analysis also indicated improved particle size distribution with values of D10, D50, and D90 as 3.971, 9.642, and 15.84 μm using ultrasonic reactor whereas in the case of conventional approach, the obtained values were 21.37, 85.94, and 172.7 μm, respectively. It was also observed that the shape of crystal is rod with sharp edges using conventional approach while plate shaped with smooth edges was the observed morphology when operated with ultrasonic reactor. The elucidated findings highlight the potential of crystallization process using ultrasonic reactor as a powerful tool for refining the crystallization process and adapting drug crystal characteristics to specific formulation requirements.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".