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Record W2042634107 · doi:10.1155/2014/312813

Effect of Nanocomposite Structures on Fracture Behavior of Epoxy‐Clay Nanocomposites Prepared by Different Dispersion Methods

2014· article· en· W2042634107 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 Nanomaterials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceNanocompositeEpoxyComposite materialDispersion (optics)Fracture (geology)

Abstract

fetched live from OpenAlex

The effects of organic modifier and processing method on morphology and mechanical properties of epoxy‐clay nanocomposites were investigated. In this study, the preparation of nanocomposites by exfoliation‐adsorption method involved an ultrasonic mixing procedure, and mechanical blending was used for in situ intercalative polymerization. The microstructure study revealed that the organoclay, which was ultrasonically mixed with the epoxy, partially exfoliated and intercalated. In contrast, organoclay remained in phase‐separated and flocculated state after the mechanical blending process. Tensile stiffness increased significantly for the nanocomposite prepared by ultrasonic dispersion method through realizing the reinforcing potential of exfoliated silicate layers. Nanocomposites with exfoliated and intercalated nanoclay morphology were ineffective in enhancing the fracture toughness whereas nanocomposites with phase‐separated and flocculated morphology have improved crack resistance predominantly by crack deflecting and pinning mechanisms.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.294
Teacher spread0.286 · 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