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Record W4399215883 · doi:10.1122/8.0000724

Newtonian coalescence in colloidal and noncolloidal suspensions

2024· article· en· W4399215883 on OpenAlexaff
Abhineet Singh Rajput, Sarath Chandra Varma, Pallavi Katre, Aloke Kumar

Bibliographic record

VenueJournal of Rheology · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Toronto
FundersScience and Engineering Research Board
KeywordsCoalescence (physics)ColloidNon-Newtonian fluidRheologyMaterials scienceNewtonian fluidColloidal particleMechanicsClassical mechanicsChemical engineeringPhysicsComposite materialAstrobiology

Abstract

fetched live from OpenAlex

Coalescence event in pendant and sessile droplets is distinguished by the formation and evolution of the liquid bridge created upon singular contact. For Newtonian droplets, the bridge radius, R, is known to evolve as R∼tb, with universal values of the power-law exponent, b, signifying the dominant governing forces. However, recent works on different subclasses of rheologically complex fluids comprising of macromolecules have highlighted the effects of additional forces on coalescence. In this work, we experimentally explore the phenomenon in distinct subclasses of rheologically complex fluids, namely, colloidal and noncolloidal suspensions, that have particle hydrodynamic interactions as the origin of viscoelasticity. Our observations suggest that such fluids have flow-dependent thinning responses with finite elasticity in shear rheology but negligible elasticity in extensional rheology. Based on these, the study extends the Newtonian universality of b=0.5 to these thinning fluids. Further, we fortify these observations through a theoretical model developed by employing Ostwald–de Waele’s constitutive law. Finally, we utilize this theoretical model to inspect the existence of arrested coalescence in generalized Newtonian fluids.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.312

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 head0.003
GPT teacher head0.211
Teacher spread0.207 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

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