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Record W4205503672 · doi:10.1002/cjce.24371

Mechanistic models for liquid binder evaporation in wet granulation

2022· article· en· W4205503672 on OpenAlexvenueno aff
Elham Heidari, Salman Movahedirad, Mohammad Amin Sobati

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporationMicroscale chemistryGranulationContact angleMass transferMaterials scienceParticle (ecology)Volume (thermodynamics)MechanicsThermodynamicsChemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

Abstract In this study, binder evaporation as a significant microscale phenomenon in fluidized bed wet granulation has been investigated in three situations: a single droplet, a sessile droplet, and a liquid bridge. Single droplet evaporation has been analytically modelled by mass transfer analysis from a spherical droplet subjected to the relative velocity of the medium and considering evaporation rate variations versus droplet diameter. Then, the sessile droplet evaporation model has been used from the literature. Finally, the liquid bridge evaporation has been modelled, and the rupture time of the bridge has been computed. The local temperature dependence of air physicochemical properties has been considered in all models. The single and sessile droplet evaporation rates have been successfully validated by the experimental data of the air–water system. The effects of operational conditions and liquid/particle properties on each evaporation event have been evaluated and quantified. The results indicate that both the higher relative velocity between the air and a single droplet and the smaller equilibrium contact angle in a constant volume of sessile droplets increase the evaporation rate. Also, increasing the length of the binder bridge reduces the rupture time.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.248

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 head0.014
GPT teacher head0.184
Teacher spread0.170 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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