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Record W2757454652 · doi:10.1002/jcc.25069

The extent of the glass transition from molecular simulation revealing an overcrank effect

2017· article· en· W2757454652 on OpenAlexafffund
François Godey, Alexandre Fleury, Aziz Ghoufi, Armand Soldera

Bibliographic record

VenueJournal of Computational Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlass transitionAmorphous solidMolecular dynamicsPhase transitionWork (physics)Natural rubberMaterials scienceThermalDomain (mathematical analysis)Volume (thermodynamics)Degrees of freedom (physics and chemistry)Heat capacityAtmospheric temperature rangeThermodynamicsThermal expansionStatistical physicsRange (aeronautics)PhysicsChemistryComposite materialPolymerMathematicsCrystallographyComputational chemistryMathematical analysis

Abstract

fetched live from OpenAlex

A deep understanding of the transition between rubber and amorphous state characterized by a glass transition temperature, T g , is still a source of discussions. In this work, we highlight the role of molecular simulation in revealing explicitly this temperature dependent behavior. By reporting the specific volume, the thermal expansion coefficient and the heat capacity versus the temperature, we actually show that the glass transition domain extends to a greater range of temperature, compared with experiments. This significant enlargement width is due to the fast cooling rate, and actually explains the difficulty to locate T g . This result is the manifestation of an overcranking effect used by high‐speed cameras to reveal slow‐motion. Accordingly, atomistic simulation offers the significant opportunity to show that the transition from the rubber state to the glass phase should be detailed in terms of the degrees of freedom freeze. © 2017 Wiley Periodicals, Inc.

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 score0.277
Threshold uncertainty score0.221

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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