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Record W184569607

Properties and Wood Bonding Capacity of Nanoclay-Modified Urea and Melamine Formaldehyde Resins

2013· article· en· W184569607 on OpenAlexfundno aff
Dong Xian, Kate Semple, Shayesteh Haghdan, Grant D. Smith

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
FundersNatural Resources Canada
KeywordsUrea-formaldehydeMaterials scienceThermosetting polymerComposite materialCuring (chemistry)Differential scanning calorimetryFormaldehydeMelamineSwellingFlexural strengthBond strengthAdhesiveChemistryLayer (electronics)Organic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Urea formaldehyde (UF) and melamine formaldehyde (MF) thermosetting resins were substituted with up to 6% nanoclay (organic modified CloisiteW30B and unmodified Nanofil W 116; Southern Clay Ltd, Austin, TX) and assessed for mixing and curing compatibility using X-ray diffraction, differential scanning calorimetry, wood lap-shear tests, and particleboard strength tests.Cloisite W 30B exfoliated fully in both resin types, whereas Nanofil W 116 showed increased spacing between platelets (intercalation) but not exfoliation.Nanoclays improved bonding strength of MF more than UF resin, and 2% nanoclay with a coupling agent in MF significantly enhanced particleboard bonding strength.Also, thickness swelling of particleboard in water decreased with up to 6% nanoclay.To decrease costs, MF resin could potentially be substituted by up to 6% nanoclay with no detrimental effect on properties.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.211
Teacher spread0.189 · 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
GenreEmpirical

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

Citations11
Published2013
Admission routes1
Has abstractyes

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