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Record W4205556610 · doi:10.1122/8.0000372

Flow-induced crystallization of polylactides

2022· article· en· W4205556610 on OpenAlexafffund
Ziyue Zhang, Savvas G. Hatzikiriakos

Bibliographic record

VenueJournal of Rheology · 2022
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCrystallizationMaterials scienceWeissenberg numberSimple shearShear rateShear flowCrystallization of polymersThermodynamicsCapillary actionShear (geology)ViscosityComposite materialPolymerPhysics

Abstract

fetched live from OpenAlex

In this work, the flow-induced crystallization of two polylactides (PLAs) with different microstructures (different l-lactic acid contents) is studied using simple shear, uniaxial extension and capillary flow experiments. In a simple shear and capillary flow, an increase in shear rate and a decrease in temperature were found to enhance the crystallization kinetics particularly for Weissenberg numbers (based on the reptation relaxation time, Wi) greater than 1 (strong flow causing chain stretching). On the other hand, in a uniaxial extensional flow, once a critical Hencky strain is achieved, crystallization starts independently of strain rate and temperature. The amount of mechanical work per unit volume imposed/dissipated onto the polymers during flow to initialize crystallization was also calculated in the simple shear, capillary, and extensional flow. The critical mechanical work for the onset of flow-induced crystallization was found to be independent of temperature and degree of molecular chain stretch (Wi) as Wi becomes greater than 1. The critical mechanical work for the onset of flow-induced crystallization in an extensional flow was found to be much smaller than that in a shear flow. The PLA sample with higher content of PLLA showed slightly higher zero-shear viscosity and a smaller thermodynamic barrier for the onset of crystallization. Finally, the degree of crystallinity increases linearly from 0% at the start of the flow-induced crystallization region and reaches a plateau at Wi equals to around 1.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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