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Record W2048156900 · doi:10.1515/hf.2007.027

Formation and properties of nanocomposites made up from solid aspen wood, melamine-urea-formaldehyde, and clay

2007· article· en· W2048156900 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

VenueHolzforschung · 2007
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsMaterials scienceUrea-formaldehydeComposite materialMelamineNanocompositeYoung's modulusIn situ polymerizationPrepolymerBall millFlexural strengthPolyurethanePolymerPolymerizationAdhesive

Abstract

fetched live from OpenAlex

Abstract Wood polymer nanocomposites were prepared from solid aspen wood, water-soluble melamine-urea-formaldehyde (MUF) resin, and silicate nanoclays. The nanofillers were ground with a ball-mill before being mixed with the MUF resin and impregnated into the wood. The water-soluble prepolymer was mixed with the nanoclays at a mixing speed of 3050 rpm for 20 min to form impregnation solutions. Wood was impregnated with resin, which polymerized in situ under certain conditions. The physical and mechanical properties of the composite and the effect of ball-milling treatment of nanofillers on these properties were investigated. Significant improvements in physical and mechanical properties, such as density, surface hardness, and modulus of elasticity, were obtained for specimens impregnated with MUF resin and nanoclay-MUF resin mixtures. Ball-mill treatment favors dispersion of the nanofillers into the wood, but also appears to interfere with particle-resin adhesion.

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 score0.008
Threshold uncertainty score0.637

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 communication0.0000.001
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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