Influence of filler selection on twin screw foam granulation
Bibliographic record
Abstract
The influence of filler selection in wet granulation was studied for the novel case where the binder is delivered as an unstable, semi-rigid aqueous foam to an extrusion process. The work primarily examined the impact of differing concentrations of microcrystalline cellulose (Avicel PH® 101) in a formulation with spray-dried α-lactose monohydrate (Flowlac® 100) in regards to wetting and granule nucleation for this relatively new technique known as continuous foam granulation. Foam stability was varied within the work to change its drainage and coarsening behavior atop these powder excipients, by use of different foamable binding agents (METHOCEL™ F4 PLV and METHOCEL™ Premium VLV) as well as by adjusting the foam quality. A static bed penetration test was first used to study the foam behavior in wetting these powders without the processing constraints of an extruder which limit possible liquid-to-solids ratios as well as introduce shear which may complicate interpretation of the mechanism. The test found that the penetration time to saturate these powders decreased as their water absorption capacity increased which in turn decreased the size of the formed nuclei. Differences in the stability of the foamed binder had minimal influence on these attributes of wetting despite its high spread-to-soak behavior. The size of granules produced by extrusion similarly demonstrated sensitivity to the increasing water absorption capacity of the filler and little dependency on foam properties. The different liquid-to-solids ratios required to granulate these different formulations inside the extruder highlighted an evolving concept of powder lubricity for continuous foam granulation.
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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 | 0.001 | 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".