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Record W2019314525 · doi:10.1002/app.30668

Ultrasonic characterization of the crystallization behavior of poly(ethylene terephthalate)

2009· article· en· W2019314525 on OpenAlexaff
Lijuan Zhao, Zhigang Sun, J. Tatibouët, Shaoyun Guo

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCrystallizationCrystallinityMaterials scienceUltrasonic sensorAmorphous solidCrystallization of polymersPolymerCrystallitePoly ethyleneEthylenePolyethylene terephthalateIsothermal processComposite materialChemical engineeringThermodynamicsCrystallographyChemistryOrganic chemistryAcoustics

Abstract

fetched live from OpenAlex

Abstract On the basis of the previous observations that the ultrasonic signals are sensitive to the crystallization of polymers (Tatibouet and Piché, Polymer 1991, 32, 3147), we have expanded our efforts to study the detail relationship between the ultrasonic signals and crystallization process in this work. The nonisothermal and isothermal crystallization of virgin poly(ethylene terephthalate) (PET) and PET samples after degradation were studied by using a specially designed pressure‐volume‐temperature (PVT) device, with which an ultrasonic detector was combined. The results showed that the evolution of the ultrasonic signals not only can be used to probe the crystallization process but also can qualitatively characterize the crystallization rate, crystallinity, crystallite size, and amorphous. DSC measurement was used to verify such results. Ultrasonic signals could be as a complementary tool to polymer chain movement and well be applied to characterize the crystallization behavior. Furthermore, the ultrasonic measurement has the potential use to characterize crystallization of products in‐line during processing (i.e., injection molding, micromoulding). © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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 score0.021
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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