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Record W2105063319 · doi:10.1002/pen.20687

Microdielectric analysis and curing kinetics of an epoxy resin system

2007· article· en· W2105063319 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymer Engineering and Science · 2007
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsEpoxyMaterials scienceCuring (chemistry)Differential scanning calorimetryGlass transitionIsothermal processComposite materialConductivityKineticsPolymer chemistryThermodynamicsPolymerPhysical chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Microdielectric analysis (DEA) was carried out to investigate the cure behavior of a bisphenol F epoxy/aromatic amine resin system using an online dielectric cure monitoring technique. Ionic conductivity measured by a microdielectric sensor under isothermal conditions was correlated to the degree of cure and glass‐transition temperature, which are determined by differential scanning calorimetry (DSC). Results obtained by isothermal DSC measurement were used to establish a cure kinetic model for the epoxy resin. Experimental results show that the ratio of the ion conductivity to the initial ion conductivity, Logσ/Logσ 0 , has a linear relation with the glass‐transition temperature. Furthermore, correlations between ion conductivity and degree of cure and cure rate are established using the best fit of the measured data. Cure behavior of the epoxy resin obtained by DEA is compared with that predicted by the cure kinetics model. Good agreement was observed. POLYM. ENG. SCI., 47:150–158, 2007. © 2007 Society of Plastics Engineers

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.

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 score0.103
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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