Impact of curing condition on pH and alkalinity/acidity of structural wood adhesives
Bibliographic record
Abstract
Abstract Nine formulations were selected for evaluating the effect of different curing methods on pH and alkalinity or acidity of various structural wood adhesives. These included four phenol–formaldehyde (PF) resins with high pH, one phenol–resorcinol–formaldehyde (PRF) resin with intermediate pH, two melamine–urea–formaldehyde (MUF) resins, and two melamine–formaldehyde (MF) resins with low pH. The four curing methods used in the study were: (1) curing at 102–105°C for 1 h (based on CSA O112.6‐1977), (2) four‐hour curing at 66°C followed by 1‐hour curing at 150°C (based on ASTM D1583‐01), (3) curing at room temperature overnight (based on ASTM D 1583‐01), and (4) cured adhesive squeezed out from glue lines of bonded shear block samples. The effect of the different methods on pH and alkalinity/acidity of the cured adhesive depended strongly on the individual adhesives. For the PF, the alkalinity was different for the different formulations in the liquid form, while in the cured form, the difference in the alkalinity depended on the curing method used. The MF and the MUF were the adhesives most affected by the method used. In particular, the MUF showed much higher cured film pH values when cured by method 2 compared to the other three methods, while both the cured MF and MUF exhibited quite variable acidity values when cured with the different methods. The PRF showed reasonably uniform cured film pH but varying acidity values when cured with the different methods. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".