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Record W3107359827 · doi:10.1021/acs.macromol.0c01688

Microstructural Rearrangements and Their Rheological Signature in Coarsening of Cocontinuous Polymer Blends

2020· article· en· W3107359827 on OpenAlexafffund
Rajas Sudhir Shah, Steven L. Bryant, Milana Trifkovic

Bibliographic record

VenueMacromolecules · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesCanada Excellence Research Chairs, Government of Canada
KeywordsRheologyMaterials sciencePolymer blendPolymerAnnealing (glass)Nonlinear systemThermodynamicsChemical physicsComposite materialChemistryCopolymerPhysics

Abstract

fetched live from OpenAlex

Domains of cocontinuous polymer blends coarsen during annealing. In general, coarsening undergoes two regimes: linear growth followed by slower nonlinear growth. While the linear regime is well understood by several theories, the number of studies on the nonlinear regime is scarce and often inconclusive. Herein, we examine the entire spectrum of the coarsening of cocontinuous polymer blends using an in situ high-temperature confocal rheology technique. By linking 4-dimensional microscopic details (time evolution of three-dimensional microscopic details) with the rheological properties, we demonstrate that the transition to the nonlinear regime is associated with the onset of droplet formation during the coarsening of these blends. Such transitions and morphological changes occurred only in the blends with high interfacial tension to zero-shear viscosity ratio (Γ/η0). This phenomenon provides a framework for comprehensive model development. By analyzing the data from the literature, we propose that there exists a critical Γ/η0 ratio beyond which the system undergoes such transitions.

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 score0.108
Threshold uncertainty score0.461

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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