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Record W2315247099 · doi:10.1021/ie400803d

Nonisothermal DSC Study of Epoxy Resins Cured with Hydrolyzed Specified Risk Material

2013· article· en· W2315247099 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.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsUniversity of Alberta
FundersAlberta Prion Research Institute
KeywordsDiglycidyl etherActivation energyEpoxyCuring (chemistry)Differential scanning calorimetryPolypropylene glycolBisphenol AOrder of reactionPolymer chemistryChemistryMaterials scienceAutocatalysisHydrolysisChemical engineeringOrganic chemistryReaction rate constantKineticsThermodynamicsCatalysis

Abstract

fetched live from OpenAlex

The effects of salt, molecular weight and viscosity, and mass ratio on the apparent activation energy of the cross-linking reaction of epoxy resins and protein hydrolysate were studied by nonisothermal differential scanning calorimetry. The Kissinger equation, the model-free isoconversional method, and the autocatalytic model were used to analyze the kinetic data. The presence of salts contributed to an increase in the apparent activation energy. The curing of epoxy resins with lower molecular weight protein hydrolysates was found to have lower activation energy and order of reaction. An increase in the concentration of curing groups resulted in a small increase in the order of reaction. The activation energy of curing bisphenol A diglycidyl ether (DGEBA), with viscosity 500–700 cP, was found to be significantly higher than the curing activation energy of polypropylene glycol diglycidyl ether (PPGDE), which has a viscosity of 50 cP.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.046
GPT teacher head0.273
Teacher spread0.228 · 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