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Record W4405634508 · doi:10.1063/5.0251452

New nucleating agents for shear-induced crystallization of polypropylene

2024· article· en· W4405634508 on OpenAlexaff
Ziyue Zhang, Yogesh S. Deshmukh, Yasir Al-Sharif, Antonios K. Doufas, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of British Columbia
FundersSaudi Basic Industries Corporation
KeywordsCrystallizationNucleationPolypropyleneMaterials scienceShear rateRheometryKineticsShear (geology)Chemical engineeringCrystallization of polymersMelting pointRheologyThermodynamicsComposite materialPolymer chemistryPhysics

Abstract

fetched live from OpenAlex

The effect of shear on the flow-induced crystallization of a polypropylene with and without nucleating agents was studied using shear rheometry. Generally, an increase in strain and strain rate or decrease in temperature is found to decrease the thermodynamic barrier for crystal formation, thus enhancing crystallization kinetics at temperatures between the melting and crystallization points. Second, the use of nucleating agents dramatically increases the crystallization and melting point of polypropylene, thus enhancing the kinetics of crystallization. Herein, we report the quiescent isothermal and shear-induced crystallization (rheology) behavior of a random copolymer polypropylene with ethylene as a co-monomer containing sorbitol nucleating agents (NA) with different degrees of polarity. The presence of sorbitol NA increases the Tm and Tc by 5 °C and 15 °C, respectively. By performing steady-shear experiments at shear rates varying from 0.001 s−1 to 1 s−1, “quiescent crystallization” and “shear-induced crystallization” regions could be identified. From both isothermal and shear-induced crystallization experiments, sorbitol-based NA with the lowest degree of polarity was found to cause the highest crystallization kinetics.

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 score0.440
Threshold uncertainty score0.319

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 head0.045
GPT teacher head0.290
Teacher spread0.245 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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