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Record W1974895428 · doi:10.1155/2013/341897

Ultrasonication Technique: A Method for Dispersing Nanoclay in Wood Adhesives

2013· article· en· W1974895428 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

VenueJournal of Nanomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversité LavalCentre de Géomatique du Québec
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsMaterials scienceSonicationComposite materialPolyvinyl acetateDispersion (optics)AdhesivePolyvinyl alcoholBond strengthMixing (physics)Matrix (chemical analysis)PolymerChemical engineering

Abstract

fetched live from OpenAlex

The efficiency of ultrasonication technique to disperse nanoclay in polyvinyl acetate (PVA) was examined. A hydrophilic nanoclay was added to PVA, and its effects on bond strength of wood joints were determined. The results of bond strength measured on block shear tests showed that nanoclay increased the bond strength of wood joints, especially in humid conditions. Atomic force microscopy (AFM) proved that it can be used to examine the quality of nanoclay dispersion in a matrix very precisely. The results of this study showed that ultrasonication technique is efficient in mixing nanoclay with the PVA matrix.

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 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 score0.115
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.285
Teacher spread0.269 · 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