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Record W4409225227 · doi:10.1021/acsnano.5c00464

Internal Microstructure Dictates Yielding and Flow of Jammed Suspensions and Emulsions

2025· article· en· W4409225227 on OpenAlexaff
Léo Gury, Mario Gauthier, Jean‐Marc Suau, Dimitris Vlassopoulos, Michel Cloître

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
FundersH2020 Marie Skłodowska-Curie ActionsEuropean Commission
KeywordsMicrostructureMaterials scienceFlow (mathematics)RheologyNanotechnologyMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

We propose a microstructural classification of jammed suspensions made of soft, deformable colloids with purely repulsive interactions. Three distinct classes of particles are identified, depending on their ability to accommodate topological constraints upon increasing concentration: emulsions with constant particle volume, noninterpenetrating microgels (without dangling ends), which deswell osmotically, and star polymers, which deswell and interpenetrate. Each class has a specific rheological response in transient and steady-state flows. The transient behavior of emulsion-like systems and noninterpenetrating microgel suspensions is characterized by a single colloidal yielding process, whereas dispersions of star-like particles exhibit both colloidal and polymeric yielding due to their fuzzy microstructure with dangling arms. The flow of emulsions and microgels or stars at relatively low concentrations is characterized by a single process that controls cooperative rearrangements. Deswelling and interpenetration mark a departure from this universality and lead to more complex flow mechanisms. This simple generic description of yielding and flow demonstrates the importance of the microstructure and at the same time serves as a powerful indicator of the internal microstructure of particles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.265

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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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 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

Citations8
Published2025
Admission routes1
Has abstractyes

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