MétaCan
Menu
Back to cohort
Record W2510008262 · doi:10.1021/acs.iecr.5b00765

In Situ Rheological Measurement of an Aqueous Polyester Dispersion during Emulsification

2015· article· en· W2510008262 on OpenAlexaff
A. Goger, Michael R. Thompson, J. L. Pawlak, David J. W. Lawton

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsXerox (Canada)McMaster University
FundersXerox
KeywordsImpellerRheologyPolyesterRheometerPolymerMaterials scienceAgitatorAqueous solutionDispersion (optics)Drop (telecommunication)Shear rateSurface tensionPulmonary surfactantShear (geology)Chemical engineeringMixing (physics)In situComposite materialChemistryThermodynamicsOrganic chemistryMechanical engineering

Abstract

fetched live from OpenAlex

Rheological analysis of a complex fluid system like an aqueous polymer dispersion can be challenging but can reveal mechanistic information as the viscous melt is emulsified. A pressurized vessel was used as a rheometer, based on the Metzner–Otto approach, to evaluate formulation variables where the developed morphology of a polyester/water mixture was shear-dependent. The parameters of the study were resin-to-water ratio (R/W), surfactant (type and concentration), and process variables of impeller speed and temperature. Transient in situ information on the system during the mixing of water into the molten polymer showed that a rapid, near-instantaneous decrease in the torque on the impeller occurred consistently around 2% water addition, related to the onset of the developed morphological state. It was observed to only demonstrate a drop in the torque for high shear rates and only with the appropriate content of surface-active species, revealing the chemical and physical parameters necessary to emulsify the polyester melt.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.304
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicFluid Dynamics and MixingFrench-language works237,207