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Record W2798094039 · doi:10.1039/c8cp00755a

Stereodynamic insight into the thermal history effects on poly(vinyl chloride) calorimetric sub-glass and glass transitions as a fragile glass model

2018· article· en· W2798094039 on OpenAlexafffund
Jean‐Mathieu Pin, Ehsan Behazin, Manjusri Misra, Amar K. Mohanty

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of GuelphDiscovery Centre
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsGlass transitionThermalVinyl chlorideMaterials scienceChemical engineeringChemical physicsThermodynamicsComposite materialChemistryPolymerPhysicsCopolymer

Abstract

fetched live from OpenAlex

The dynamic thermal history impact of poly(vinyl chloride) (PVC) has been explored for a wide range of pre-cooling rates, from 1 to 30 °C min-1. A first macroscopic insight into the dynamic thermal history influence has been highlighted through a decrease in the apparent activation energy (Eapp) in the first stage of the glass transition. The overall glass transition Eapp surface was successfully modeled in a polynomial fashion regarding the pre-cooling range. Raman scattering was used to associate the Eapp variations along the glass transition conversion with the stereochemistry evolution during the polymeric relaxation. Herein, the selection of atactic PVC as the polymer model permits us to monitor the glassy polymer segment stereodynamics during the heating ramp through the C-Cl stretching. The intermolecular H-Cl dipole interactions, as well as intramolecular conformational reorganizations among syndiotactic, isotactic and heterotactic polymer sequences, have been associated with non-cooperative and cooperative motions, i.e. the β- and α-process, respectively. The fruitful comparison of the two extreme values of the pre-cooling rates permits us to propose a thermokinetic scenario that explains the occurrence, intensity, and inter-dependence of β- and α-processes in the glassy state and during the glass transition. This scenario could potentially be generalized to all the other polymeric glass-formers.

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 categoriesMeta-epidemiology (narrow)
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 score0.073
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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