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Record W1996577406 · doi:10.1002/pen.11237

The effects of kneading block design and operating conditions on distributive mixing in twin screw extruders

2000· article· en· W1996577406 on OpenAlexafffund
Gifford Shearer, Costas Tzoganakis

Bibliographic record

VenuePolymer Engineering and Science · 2000
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceResidence time distributionMixing (physics)PolypropyleneDistributive propertyPlastics extrusionSuccinic anhydridePolymerResidence time (fluid dynamics)Shear rateChemical engineeringVolumetric flow rateComposite materialFlow (mathematics)RheologyThermodynamicsMechanicsMathematics

Abstract

fetched live from OpenAlex

Abstract A mixing limited interfacial reaction between polymer tracers was used to directly measure the distributive mixing performance of a co‐rotating twin screw extruder during melt‐melt blending of polypropylene. The reaction between the polymer tracers, which are low molecular weight succinic anhydride and primary amine terminally functionalized polymer chains, was followed using Fourier‐Transform Infrared Spectroscopy (FT‐IR). Experiments were completed to determine the effects of flow rate, screw speed, and kneading block design on the distributive mixing performance and the residence time distribution (RTD). The only RTD variable that was significantly affected by the experimental factors was the average residence time. Distributive mixing with neutral and reverse kneading blocks was controlled by the average residence time, the fully filled volume, and the shear rate. Conversely, the mixing performance of a forward kneading block did not follow the same trends.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations29
Published2000
Admission routes2
Has abstractyes

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