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Record W1993088139 · doi:10.3390/ijms16059196

A Polymer Visualization System with Accurate Heating and Cooling Control and High-Speed Imaging

2015· article· en· W1993088139 on OpenAlexafffund
Anson Wong, Yanting Guo, Chul B. Park, Nan Zhou

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrystallinityMaterials scienceNucleationCrystal (programming language)PolymerPolypropyleneDifferential scanning calorimetryCrystal growthComposite materialCrystallographyChemistryThermodynamicsComputer science

Abstract

fetched live from OpenAlex

A visualization system to observe crystal and bubble formation in polymers under high temperature and pressure has been developed. Using this system, polymer can be subjected to a programmable thermal treatment to simulate the process in high pressure differential scanning calorimetry (HPDSC). With a high-temperature/high-pressure view-cell unit, this system enables in situ observation of crystal formation in semi-crystalline polymers to complement thermal analyses with HPDSC. The high-speed recording capability of the camera not only allows detailed recording of crystal formation, it also enables in situ capture of plastic foaming processes with a high temporal resolution. To demonstrate the system's capability, crystal formation and foaming processes of polypropylene/carbon dioxide systems were examined. It was observed that crystals nucleated and grew into spherulites, and they grew at faster rates as temperature decreased. This observation agrees with the crystallinity measurement obtained with the HPDSC. Cell nucleation first occurred at crystals' boundaries due to CO₂ exclusion from crystal growth fronts. Subsequently, cells were nucleated around the existing ones due to tensile stresses generated in the constrained amorphous regions between networks of crystals.

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 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 score0.136
Threshold uncertainty score0.378

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)0.0000.000

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 head0.011
GPT teacher head0.272
Teacher spread0.261 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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