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Record W2141627436 · doi:10.1002/app.29170

Investigation of influence factors in electron beam curing of epoxy resins using a calorimetry technique

2008· article· en· W2141627436 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

VenueJournal of Applied Polymer Science · 2008
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPhotoinitiatorCuring (chemistry)EpoxyMaterials scienceIrradiationComposite materialPolymerCalorimetryPolymer chemistryMonomer

Abstract

fetched live from OpenAlex

Abstract Because of the complexity of the electron beam (EB) curing process, current understanding of EB curing of polymer resins and composites is limited. This article describes an investigation of different factors affecting EB curing of epoxy resin such as dose rate, time interval between irradiation doses, moisture, and photoinitiator concentration using a calorimetry technique. Results show that higher dose rate resulted in a higher and faster temperature increment in the uncured resin samples, and thus a higher degree of cure. In the multiple‐step EB irradiation, a shorter time interval between irradiation doses resulted in higher temperature in the resin samples and therefore higher degree of cure. Results indicate that moisture could delay crosslinking reaction in the early stages of the cure reaction, but accelerates it later in the curing process. Given a reasonable percentage of photoinitiator, experiments confirmed that samples with higher photoinitiator concentration reach higher degree of cure under same EB irradiation conditions. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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 score0.007
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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