Curing process of powdered phenol–formaldehyde resol resins and the role of water in the curing systems
Bibliographic record
Abstract
Abstract The curing behavior of two kinds of commercial powdered resol phenolic resins was studied by differential scanning calorimetry. Liquid‐state 13 C‐NMR spectroscopy was used to aid in understanding the curing behavior by detecting the structure of powdered resins. The reaction mechanism was interpreted with the dependency of activation energy on the degree of conversion. The results indicate that there are differences in the curing mechanism between core and face phenolic resins. The curing process of core resin was faster than that of face resin at the same reaction temperature. The water added in the curing system played an important role of plasticizer or diluent according to different curing stages and water content. In the initial curing stage, water mainly diluted the system and retarded the curing reactions. However, at the higher degrees of conversion, water played the role of plasticizer to decrease the effect of diffusion on the curing reactions to make the curing reactions more complete. The excess water added in the curing system played the role of diluent at almost all stages during the curing process. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 1371–1378, 2003
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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) | 0.000 | 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) | 0.001 | 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".