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Record W2011300590 · doi:10.1002/pen.22150

Shear‐induced crystallization of isotactic polypropylene studied by simultaneous light intensity and rheological measurements

2011· article· en· W2011300590 on OpenAlexaff
Jen Shueng Tiang, John M. Dealy

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsMcGill University
FundersNational Institute of Standards and TechnologyDow Chemical Company
KeywordsMaterials scienceRheometerShear rateCrystallizationTacticityComposite materialNucleationVolume fractionSpherulite (polymer physics)Shear (geology)ViscoelasticityRheologyShear stressPolymerPolymerizationChemical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract We investigate the effects of high shear rate and a melt‐insensitive, organophosphate nucleating agent on shear‐induced crystallization of four isotactic polypropylenes using a sliding plate rheometer under isothermal, low‐supercooling conditions. We used a bifurcated optical fiber probe to measure light intensity and a shear stress transducer to monitor the simultaneous viscoelastic response to small‐amplitude oscillatory shear. The two techniques complement each other; at early times of crystallization, large attenuation in the light intensity is observed, whereas during the later stages, a major change in the viscoelastic response occurs due to the growing volume fraction of spherulites. In contrast to quiescent crystallization, the nucleation pathway of nucleated polymers after a brief, strong shear is little influenced by the nucleating agent but strongly affected by molecular weight. The early kinetics of non‐nucleated polymers is more strongly enhanced by shear than that of nucleated polymers. Increasing either shear rate or strain accelerates crystallization, and we found the product of shear rate and strain to be useful for correlating our data. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers

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 score0.005
Threshold uncertainty score0.424

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 head0.041
GPT teacher head0.226
Teacher spread0.184 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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