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Record W1971547040 · doi:10.1002/aic.14717

Progression of wet granulation in a twin screw extruder comparing two binder delivery methods

2014· article· en· W1971547040 on OpenAlexafffund
Huiying Li, Michael R. Thompson, K.P. O’Donnell

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaDow Chemical Company
KeywordsGranulationMicrocrystalline celluloseWettingMaterials scienceGranule (geology)Composite materialPlastics extrusionParticle sizeMethyl celluloseCelluloseChemical engineeringEngineering

Abstract

fetched live from OpenAlex

The two available wetting methods for twin screw granulation, namely foam delivery and liquid injection, were studied in detail by examining granule development along the screws as powder formulation and screw design were varied. Granulation profiles were determined by particle size analysis of samples along the screws collected using the “screw pullout” technique. Analysis of the particle size and porosity of produced granules revealed only minor differences between the two methods of wetting despite the larger dropsize of liquid injection compared to foam delivery. Excipients like microcrystalline cellulose or hydroxypropyl methyl cellulose with poor spreading properties, quantified by their specific penetration time and nucleation ratio, made the differences more apparent. The general similarities in granulation independent of wetting method implied that binder dispersion in an extruder was dominated by mechanical dispersion. Screw design (i.e., location of kneading block) had the dominant effect on the granulation process in this study. © 2014 American Institute of Chemical Engineers AIChE J , 61: 780–791, 2015

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.313
Teacher spread0.292 · 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 designSimulation or modeling
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

Citations8
Published2014
Admission routes2
Has abstractyes

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