Investigation of the effect of impeller speed on granules formed using a PMA-1 high shear granulator
Bibliographic record
Abstract
Impeller speed was varied from 300 to 1500 rpm during the wet high shear granulation of a placebo formulation using a new vertical shaft PharmaMATRIX-1 granulator. The resulting granules were extensively analysed for differences caused by the varying impeller speed with emphasis on flowability. Microscopy showed that initial granules were formed primarily from microcrystalline cellulose at all tested impeller speeds. At low impeller speed of 300 rpm in the "bumpy" flow regime, forces from the impeller were insufficient to incorporate all the components of the formulation into the granules and to promote granule growth to a size that significantly improved flowability. The "roping" flow regime at higher impeller speeds promoted granule growth to a median particle size of at least 100 µm that improved the flowability of the mixture. Particle size distribution measurements and advanced indicators based on avalanching behavior, however, showed that an impeller speed of 700 rpm produced the largest fraction of optimal granules with the best flowability potential. This impeller speed allowed good development of "roping" flow for sufficient mixing, collision rates and kinetic energy for collisions while minimizing excessive centrifugal forces that promote buildup around the bowl perimeter.
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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) | 0.000 | 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".