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Record W2522092243 · doi:10.1002/adv.21775

Measurements and Comparison to Predictions of Viscosity of Heavily Filled HDPE with Natural Fibers

2016· article· en· W2522092243 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Polymer Technology · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceHigh-density polyethyleneConical surfaceComposite materialViscosityDissipationShear thinningPower lawFiller (materials)RheologyConsistency (knowledge bases)RheometryExtrusionMechanicsThermodynamicsPolyethylenePhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Composites of rice hulls at various levels of loadings in recycled HDPE were characterized rheologically using a conical extrusion die and small amplitude oscillatory shear (SAOS) measurements. Although the results do not match, due to the invalidity of the Cox–Merz rule for such composites, both types of measurements indicate that increasing the filler concentration results in significantly higher viscosities and increased shear thinning. The errors associated with the use of a conical die due to viscous dissipation are assessed through computer simulations. They are small for relatively small throughputs (small pressure drops). The experimental results for viscosity are compared to predictions of the Einstein–Batchelor equation for suspensions of spherical particles, the Guth–Simha equation, and the empirical Krieger–Dougherty correlation, for filler loadings of up to 70% by weight. The comparison indicates that reasonable approximations of the consistency index of the power law viscosity model are possible through using the above expressions for loadings up to perhaps 40–50% by weight.

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.

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 score0.545
Threshold uncertainty score0.255

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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