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Record W2766233338 · doi:10.1021/acs.iecr.7b03370

Effect of Viscosity on Solvent-Free Extrusion Emulsification: Molecular Structure

2017· article· en· W2766233338 on OpenAlexafffund
A. Goger, Michael R. Thompson, J. L. Pawlak, Mark A. Arnould, A. Klymachyov, David J. W. Lawton

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsXerox (Canada)McMaster University
FundersMcMaster UniversityXerox
KeywordsExtrusionViscositySolventChemistryChemical engineeringMaterials scienceChromatographyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A new continuous emulsification technique known as solvent-free extrusion emulsification (SFEE) was recently introduced to prepare submicron particles (100–500 nm) from high viscosity polymers (100–1000 Pa·s) with a twin screw extruder. The present study examined the influence of matrix viscosity on its dispersion mechanism using cross-linked polyester as a viscosity modifier. The investigation used an inline rheometer for transient and steady state viscosity measurements, and offline characterizations including Soxhlet extraction, colorimetric titration, and particle size analysis. Though it remained possible to produce particles close to their target size of 100–200 nm, particle size was notably increased by varying the matrix viscosity from 250 Pa·s for the neat polyester up to 630 Pa·s with the added modifier. The results point to thicker striated lamellae from less effective mixing prior to phase inversion when the matrix viscosity was increased without a corresponding increase in surface active species. The study was primarily focused on the dispersion zone revealing that a longer mixing zone for dispersing the water into the polyester was beneficial to forming smaller particles. A preliminary investigation on the downstream dilution zone was included, finding that a longer region produced smaller particles as well so long as the water temperature remained high.

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)0.005
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 score0.043
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 science0.0010.001
Research integrity0.0010.001
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 head0.337
Teacher spread0.297 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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