MétaCan
Menu
Back to cohort
Record W2346192452 · doi:10.1021/acs.macromol.6b00212

Controlling the Morphology of Immiscible Cocontinuous Polymer Blends via Silica Nanoparticles Jammed at the Interface

2016· article· en· W2346192452 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMinnesota Supercomputing Institute, University of MinnesotaAmerican Chemical Society Petroleum Research FundDow Chemical Company
KeywordsMaterials scienceWettingNanoparticlePolymer blendComposite materialPolymerAnnealing (glass)PolyethyleneScanning electron microscopeChemical engineeringCopolymerNanotechnology

Abstract

fetched live from OpenAlex

Cocontinuous polymer blends have wide applications. They can form conductive plastics with improved mechanical properties. When one phase is extracted, they yield porous polymer sheets, which can be used as filters or membrane supports. However, the cocontinuous morphology is intrinsically unstable due to coarsening during static annealing. In this study, silica nanoparticles, ∼100 nm diameter, with different wetting properties were melt compounded in polyethylene/poly(ethylene oxide) blends. Calculated wetting coefficients of these particles match well with their phase contact angles and their locations in the blends. We demonstrated that a monolayer of particles jamming at interfaces can effectively suppress coarsening and stabilize the cocontinuous morphology. We also correlated the wettability of individual particles at interface to their coarsening suppression ability and found that the most hydrophobic silica nanoparticle is the most effective to arrest coarsening. Moreover, during annealing, we used the rheological dynamic time sweep, a facial but sensitive method, to relate the morphology change with particle dispersion on the interface. We further corroborated these measurements by scanning electron microscopy and confocal microscopy imaging.

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.

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 score0.007
Threshold uncertainty score0.545

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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