Differential scanning calorimetry characterization of urea–formaldehyde resin curing behavior as affected by less desirable wood material and catalyst content
Bibliographic record
Abstract
Abstract Differential scanning calorimetry was applied to investigate the curing behavior of urea–formaldehyde (UF) resin as affected by the catalyst content and several less desirable wood materials (e.g., wood barks, tops, and commercial thinnings). The results indicate that the reaction enthalpy of UF resin increased with increasing catalyst content. The activation energy and peak temperature of the curing UF resin generally decreased with increasing catalyst content at lower levels of catalyst content. However, with further increases in catalyst content, the changes in the activation energy and peak temperature were very limited to nonexistent. The hydrolysis reaction of the cured UF resin occurred during the latter stages of the curing process at both lower level (<0.2%) and higher level (>0.7%) catalyst contents. This indicates that there existed an optimal range of catalyst content for the UF resin. The curing enthalpy of the UF resin decreased with increasing wood raw materials present due to the effect of diffusion induced by the wood materials and the changes in the phase of the curing systems. This suggests that the curing reactions reached a lower final degree of conversion for the wood–resin mixtures than for the UF resin alone. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 2027–2032, 2005
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".