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Record W2037487700 · doi:10.1002/cjce.22146

Viscosity models for concentrated suspensions of solid core‐porous shell particles

2014· article· en· W2037487700 on OpenAlexaffvenue
Rajinder Pal

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePorosityVolume fractionViscosityComposite materialPermeability (electromagnetism)Suspension (topology)Porous mediumRelative viscosityComposite numberShell (structure)SPHERESCore (optical fiber)ChemistryMembrane

Abstract

fetched live from OpenAlex

New models are developed for the low‐shear viscosity of concentrated suspensions of solid core‐porous shell particles taking into consideration the effects of the thickness and permeability of shell surrounding the solid core of the core‐shell particles. The relative viscosity of suspension depends on factors such as relative thickness of the porous shell, size of the composite (solid core‐porous shell) particles, permeability of the shell, and volume fraction of the core particles. For given relative thickness of porous layer and size of the composite particles, the relative viscosity of suspension decreases substantially with the increase in the permeability of the porous layer at a fixed volume fraction of core particles. Upon shrinking of the solid core, the relative viscosity of suspension exhibits a large increase provided that the size of the composite particles, the permeability of the porous shell, and the volume fraction of core particles are all kept constant. The proposed models are evaluated using data available on low‐shear viscosity of concentrated suspensions of three different types of particles: porous particles, solid core–hairy shell particles, and hard spheres.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.386

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 head0.020
GPT teacher head0.209
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations2
Published2014
Admission routes2
Has abstractyes

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