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Record W2320186801 · doi:10.1021/mz4006217

When Does a Glass Transition Temperature Not Signify a Glass Transition?

2014· article· en· W2320186801 on OpenAlexaff
James A. Forrest, Kari Dalnoki‐Veress

Bibliographic record

VenueACS Macro Letters · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsMcMaster UniversityPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsGlass transitionMaterials scienceTransition (genetics)Composite materialPolymerChemistry

Abstract

fetched live from OpenAlex

We present a simple model that links enhanced mobility at the free surface to the dilatometric glass transition temperature, T g in thin films. The model shows that what is typically measured as a dilatometric T g, characterized by the hallmark “kink” in the plot of film thickness versus temperature, only represents the dynamics of an infinitesimally thin layer of the sample. In other words, the measured dilatometric T g value in thin films is no longer a good reporter of the dynamics. Calculations based on the model are found to agree with a vast body of thin film T g measurements. While mathematically simple, the model contains all the necessary physics of a near surface layer with enhanced dynamics and a length scale over which the surface dynamics monotonically varies from surface enhanced to bulk-like. The model demonstrates that the typical dilatometric measurement of the glass transition is not necessarily a real glass transition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.012
Scholarly communication0.0030.011
Open science0.0020.001
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.006
GPT teacher head0.196
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations78
Published2014
Admission routes1
Has abstractyes

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