Influence of Process Variable and Physicochemical Properties on the Granulation Mechanism of Mannitol in a Fluid Bed Top Spray Granulator
Bibliographic record
Abstract
This study investigated the influence of specific process variables, including the hydroxypropyl cellulose (HPC) binder solution atomization, on the fluidized bed top spray granulation of mannitol. Special attention was given to the relationship between wetting and the granule growth profile. The atomization of the HPC binder solution using a binary nozzle arrangement produced droplets of decreasing size as the atomization pressure was increased, while changes in the spray rate had little effect on the mean droplet size. Increasing the HPC binder concentration from 2 to 8% w/w increased the binder droplet size and was most likely attributed to higher solution viscosity. The top spray granulation of mannitol showed induction type growth behavior. Process conditions like high spray rate, low fluidizing air velocity and binder solution concentration that promote the availability of HPC binder solution at the surface of the particles appeared to be key in enhancing nucleation and growth of the granules. Increasing the bed moisture level, up to a certain value, reduced the contribution of attrition to the overall growth profile of the granule and, more significantly, produced less granule breakage on drying. It was observed that the mean granule size could be reduced as much as 40% between the end of granulation and the end of drying for lower initial bed moisture level despite a shorter drying phase. High atomization pressure, especially when maintained during the drying phase, contributed substantially to granule breakage.
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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) | 0.000 | 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".