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Record W2020984168 · doi:10.1002/app.23859

New method for the synthesis of clay/epoxy nanocomposites

2006· article· en· W2020984168 on OpenAlexaff
H. Wang, Suong V. Hoa, Paula M. Wood‐Adams

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsConcordia University
Fundersnot available
KeywordsOrganoclayEpoxyMaterials scienceExfoliation jointNanocompositeComposite materialMontmorilloniteDispersion (optics)RheologyCompressive strengthGrapheneNanotechnology

Abstract

fetched live from OpenAlex

Abstract A new liquid–liquid method for the synthesis of epoxy nanocomposites was developed. This new method improved the dispersion and exfoliation of the organoclay in the polymer matrix, thus improving the end‐use properties. The microstructure and physical properties of the clay/epoxy nanocomposite synthesized by the new method were studied. Rheological tests of the uncured epoxy–organoclay system demonstrated that this method resulted in a great increase in viscosity, much more than the most commonly used direct‐mixing method. The Krieger–Dougherty model successfully described the dispersion of the clay layers in the uncured epoxy. In the 5 wt % organoclay nanocomposite, compressive tests on the cured samples showed that there was a 45% increase in the maximum strength, a 10% increase in the yield strength, and a 26% increase in the modulus over the pure epoxy–amine cured system. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 4286–4296, 2006

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2006
Admission routes1
Has abstractyes

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