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Record W2007206550 · doi:10.1002/app.33210

Evaluation of block shear properties of selected extreme‐pH structural adhesives by short‐term exposure test

2010· article· en· W2007206550 on OpenAlexaff
Yaolin Zhang, Xiangming Wang, Romulo Casilla, Paul Cooper, Zeen Huang, Xiaodong Wang

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of TorontoCanadian Forest ServiceFPInnovations
Fundersnot available
KeywordsAdhesiveFormaldehydeUrea-formaldehydeMelamineComposite materialShear strength (soil)PhenolMaterials scienceResorcinolChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nine structural adhesives with varying pH were selected to examine the effect of adhesive pH on wood–adhesive bond quality. The adhesives evaluated included four highly alkaline phenol–formaldehyde, one intermediate pH phenol–resorcinol–formaldehyde, two acidic melamine–urea–formaldehyde, and two acidic melamine–formaldehyde resins. Block shear specimens were prepared using Douglas‐fir and black spruce wood. The adhesive performance was evaluated by measuring the shear properties (strength and wood failure) of the specimens tested at the dry and vacuum–pressure–redry (VPD) conditions. Adhesive pH, test condition, and wood species showed significant effects on shear properties. The different adhesives performed differently at the dry and VPD conditions. The high‐pH adhesives (phenol–formaldehyde and phenol–resorcinol–formaldehyde) showed similar high wood failures at both test conditions and performed better than the low‐pH adhesives (melamine–formaldehyde and melamine–urea–formaldehyde), especially after the VPD conditioning. The low‐pH adhesives showed high wood failure at the dry condition, but wood failure decreased significantly after VPD conditioning for both species, indicating that the low‐pH adhesives were less durable than the high‐pH adhesives. High‐pH adhesives did not have a negative impact on the strength of the bonded specimens. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

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 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 score0.008
Threshold uncertainty score0.379

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 communication0.0000.000
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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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 teacher head, 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

Citations5
Published2010
Admission routes1
Has abstractyes

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