Evaluation of block shear properties of selected extreme‐pH structural adhesives by short‐term exposure test
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".