Phenol‐urea‐formaldehyde cocondensed resol resins: Their synthesis, curing kinetics, and network properties
Bibliographic record
Abstract
Abstract Several phenol‐urea‐formaldehyde (PUF) cocondensed resol resins were synthesized by different procedures. The curing kinetics and network properties of these PUF resins were examined by differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA). A kinetic study indicated that the activation energy values of PUF resins are generally higher than those of phenol‐formaldehyde (PF) resins during curing processes, but the curing rates of PUF resins are faster than those of PF resins. The pH values of PUF systems have a significant influence on the rate constants, although they affect the activation energy very slightly. Moreover, the dependence of activation energy on the conversion showed that there are more individual reactions with different activation energies occurring during the curing processes in PUF resins than in PF resins. The decomposition of methylene ether bridges to form methylene bridges probably occurs at high temperature in PUF resins. DMTA data indicated that the network rigidity of PUF resins is slightly lower than that of PF resin. The gel point and Ttan δ2 transition measured by DMTA were consistent with the kinetic results obtained from the DSC data, but they were also related to the physical and mechanical properties of the network, especially with regard to the Ttan δ2 transition. © 2003 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 41: 1929–1938, 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".