The Ability of Wood to Buffer Highly Acidic and Alkaline Adhesives
Bibliographic record
Abstract
The ability of wood to buffer and mitigate the effects of strongly acidic or alkaline environments produced near the glue line by extreme pH structural adhesives was evaluated. The pH values of wood, cured adhesives, and mixtures of the two in water slurries were determined for different wood types. The pHs of slurries of seven highly alkaline phenol-formaldehyde adhesives were lowered when the adhesive was cured in the presence of wood dust with effects increasing with the proportion of wood in the mixture. The "acidities" or amounts of alkali needed to adjust the slurries to pH 12.5 were relatively high for all species because of weak acid groups in wood that dissociate at pH greater than 8. This explains the ability of wood to buffer highly alkaline adhesives. The pHs of slurries of two acidic melamine-urea-formaldehyde adhesives increased in the presence of wood, but the effect was less significant compared with the alkaline adhesives. Similarly, the "alkalinities" or amounts of acid required to adjust the slurries to pH 3 were relatively low. Aspen veneer samples had a greater effect on adhesive pH than spruce and Douglas-fir. These effects will help mitigate potentially adverse effects of strongly alkaline or acidic adhesives on wood adhesive bond strength.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".