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Record W2341536190 · doi:10.1002/cjce.22504

Cure kinetics characterization of soy‐based epoxy resins for infusion moulding process

2016· article· en· W2341536190 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsFord Motor Company (Canada)University of Toronto
FundersCentre for Bio-composite and Biomaterial ProcessingUniversity of Toronto
KeywordsEpoxyDiglycidyl etherKineticsDifferential scanning calorimetryMaterials scienceBisphenol AIsothermal processEnthalpyComposite materialUltimate tensile strengthChemical engineeringPolymer chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract This study presents the kinetics of the reaction of diglycidyl ether of bisphenol A (DGEBA) based epoxy resin in the presence of epoxidized soybean oil (ESO) cured with triethylene tetramine (TETA). An isothermal differential scanning calorimetric (DSC) study is carried out to propose a model to analyze the cure kinetics of bio‐based resin. The proposed model is also compared with the Kamal model. The results clearly highlight that the proposed model attains a reasonable percentage of improvement in predicting cure data. Adding ESO to the conventional system decreases the reaction enthalpy and increases the activation energy of the system. The outcome of the study confirms the feasibility of the proposed material system for an infusion process. Further, tensile property improvement is obtained through using an infusion process for the proposed material system.

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 score0.032
Threshold uncertainty score0.378

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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