Effects of conditioning exposure on the pH distribution near adhesive-wood bond lines.
Bibliographic record
Abstract
The pH distribution near the adhesive-wood bond line in black spruce (Picea mariana) and Douglas-fir (Pseudotsuga menziesii) bonded with various acidic and alkaline adhesives was investigated. For alkaline adhesives, exposure to moderate to high RH conditions and to accelerated aging treatments such as vacuum-pressure-dry or water soaking resulted in diffusion of hydroxyl ions (OH-) away from the bond line and decreased alkalinity. A moderate increase of pH in the bond line was also observed for acidic adhesives, and under very wet conditions, hydrogen ions (H+), also diffused away from the bond line. Spruce and Douglas-fir samples exposed to strongly acidic and alkaline buffered solutions for 3 mon did not show appreciable changes in chemical composition by wet chemical analysis. FTIR attenuated total reflectance spectra of samples bonded with an alkaline adhesive and exposed to either dry or wet conditions showed dissociation of carboxylic acid groups in hemicelluloses (decreased absorbance at 1735 cm -1) to a distance of 150-300 μm from the center of adhesive bond lines. No effects on wood chemistry were observed around acidic adhesive bond lines. In summary, wood bonded with high-pH adhesives and exposed to wet service conditions rapidly reached moderate pH conditions in the bond line because of the high rate of OH- diffusion in wood and the acidic buffering capacity of wood. This mitigated the effects of high pH in these alkaline adhesives. A similar but less effective process occurred with acidic adhesives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".