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Record W4386898262 · doi:10.1063/5.0168114

Cox–Merz rules from general rigid bead-rod theory

2023· article· en· W4386898262 on OpenAlexfundno aff
M. A. Kanso, Myong Chol Pak, A. Jeffrey Giacomin

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
FundersCanada Research Chairs
KeywordsPhysicsViscosityShear rateShear (geology)RodClassical mechanicsMechanicsThermodynamicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The value of this work is in its macromolecular explanations of both Cox–Merz rules, thus of when to expect them to work. For polymeric liquids and their solutions, the measured values of the steady shear viscosity and the magnitude of the complex viscosity often equate, within experimental error, when compared at common shear rate (in units of t−1) and angular frequency (in units of rad t−1). Called the first Cox–Merz rule, this remarkable empiricism, with one exception, has defied most macromolecular explanations. This one exception is the suspension of multi-bead rods and its special case of rigid dumbbells. The second Cox–Merz rule equates approximately the slope of the first derivative of steady shear viscosity with respect to shear rate with the real part of the complex viscosity when compared at common shear rate (in units of t−1) and angular frequency (in units of rad t−1). In this paper, we explain both Cox–Merz rules for all axisymmetric macromolecules, be they prolate or oblate, of almost any lopsidedness. Furthermore, through the lens of general rigid bead-rod theory, we define under what conditions these rules do not apply. Specifically, the first Cox–Merz rule fails when the macromolecules are too oblate.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0020.005
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.003

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations5
Published2023
Admission routes1
Has abstractyes

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