Influence of the synthesis conditions on the curing behavior of phenol–urea–formaldehyde resol resins
Bibliographic record
Abstract
Abstract The curing behavior of synthesized phenol–urea–formaldehyde (PUF) resol resins with various formaldehyde/urea/phenol ratios was studied with differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The results indicated that the synthesis parameters, including the urea content, formaldehyde/phenol ratio, and pH value, had a combined effect on the curing behavior. The pH value played an important role in affecting the shape of the DSC curing curves, the activation energy, and the reaction rate constant. Depending on the pH value, one or two peaks could appear in the DSC curve. The activation energy was lower when pH was below 11. The reaction rate constant increased with an increase in the pH value at both low and high temperatures. The urea content and formaldehyde/phenol ratio had no significant influence on the activation energy and rate constant. DMA showed that both the gel point and tan δ peak temperature ( T tanδ ) had the lowest values in the mid‐pH range for the PUF resins. A different trend was observed for the phenol–formaldehyde resin without the urea component. Instead, the gel point and T tanδ decreased monotonically with an increase in the pH value. For the PUF resins, a high urea content or a low formaldehyde/phenol ratio resulted in a high gel point. The effect of the urea content on T tanδ was bigger than that on the gel point because of the reversible reaction associated with the urea component. Too much formaldehyde could lead to more reversible reactions and a higher T tanδ value. The effects of the synthesis conditions on the rigidity of the cured network were complex for the PUF resins. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 95: 1368–1375, 2005
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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 | 0.001 | 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 | 0.000 | 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".