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Record W4200284306 · doi:10.1177/00952443211060400

Thermal, mechanical, and electrical properties of difunctional and trifunctional epoxy blends with nanoporous materials

2021· article· en· W4200284306 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 Elastomers & Plastics · 2021
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceEpoxyNanocompositeDynamic mechanical analysisThermal stabilityNanoporousThermogravimetryComposite materialThermogravimetric analysisFourier transform infrared spectroscopyChemical engineeringPolymerNanotechnology

Abstract

fetched live from OpenAlex

In the present study, the aim is to synthesize the particulate nanocomposites with difunctional and trifunctional epoxy blend as matrix and synthesized nanoporous materials as fillers. Organic/inorganic hybrid networks were prepared by the novel solvent free method. Viscoelastic, thermal, and electrical properties of di- and trifunctional epoxy and the effect of different nanoparticles in the particulate nanocomposites have been studied by dynamic mechanical analyzer, thermogravimetry (TGA), and dielectric strength. Epoxy mixed with different compositions of TGPAP and particulate nanocomposites by the addition of different types of nanomaterials shows higher storage modulus than the pure epoxy. The addition of TGPAP and nanofillers decreases the thermal stability of epoxy matrix. The evolved gas analysis (TG-FTIR) was also done in order to study the products formed during degradation. An increase in dielectric strength and impact strength (4%) was also observed in the particulate nanocomposites.

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 score0.011
Threshold uncertainty score0.455

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 head0.008
GPT teacher head0.178
Teacher spread0.169 · 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