MétaCan
Menu
Back to cohort
Record W2021656856 · doi:10.3109/03639045.2011.653361

Investigation of the effect of impeller speed on granules formed using a PMA-1 high shear granulator

2012· article· en· W2021656856 on OpenAlexaff
Ryan Logan, Lauren Briens

Bibliographic record

VenueDrug Development and Industrial Pharmacy · 2012
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
Fundersnot available
KeywordsImpellerGranulationGranule (geology)Materials scienceMicrocrystalline celluloseComposite materialSlip factorShear (geology)Particle sizeMechanicsCelluloseChemical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.245
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueDrug Development and Industrial PharmacySame topicGranular flow and fluidized bedsFrench-language works237,207