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Record W2611145108 · doi:10.1002/pen.24613

Inline rheological behavior of dispersed water in a polyester matrix with a twin screw extruder

2017· article· en· W2611145108 on OpenAlexaff
A. Goger, Michael R. Thompson, J. L. Pawlak, Mark A. Arnould, A. Klymachyov, R. Sheppard, David J. W. Lawton

Bibliographic record

VenuePolymer Engineering and Science · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsXerox (Canada)McMaster University
FundersXerox
KeywordsRheologyMaterials scienceExtrusionPolyesterPlastics extrusionViscometerPolymerViscosityComposite material

Abstract

fetched live from OpenAlex

Solvent‐free extrusion emulsification (SFEE) is a complex process using twin‐screw extrusion to prepare solid‐liquid dispersions of high viscosity polymers and has received little study to date on its inherent mechanisms. To gain rheological insights into the earliest stage of SFEE as the interfacial boundary between water and polymer grows, prior to phase inversion, an inline orifice‐plate type viscometer is introduced to monitor transient behavior over a wide range of viscosities. The presented work examines rheological changes of a polyester‐water system produced by varying two factors thought to significantly control the final state of the dispersion, specifically polar group contributions to surface energy and viscosity. A processing modifier was combined with the polyester to study the influence of these two factors. The inline viscometer revealed an abrupt transition in viscosity occurred with the developed state of water dispersion, confirming observations of a prior batch study. Analysis of the rheological response indicated that a higher polar surface energy contribution had the greatest influence on the state of this transition, and that a steeper transition was related to greater incorporation of water within the polyester matrix. POLYM. ENG. SCI., 2017. © 2017 Society of Plastics Engineers

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.351

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 studies0.0000.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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