Effect of wood on the curing behavior of commercial phenolic resin systems
Bibliographic record
Abstract
Abstract Differential scanning calorimetry (DSC) was used to study the effect of wood on the curing behavior of two types of commercial oriented‐strand‐board phenolic resins. DSC analysis showed that the curing behavior of the core resin differed significantly from that of the face resin in terms of the peak shape, peak temperature, and activation energy. The addition of wood to the resins moved the two separated peaks in the DSC curves of the core resin adjacent to each other. It also accelerated the addition reactions in the curing processes of both the core and face resins. The two peaks in the DSC curves were the result of the high pH values of the resins. These two peaks became either jointed together or overlapped when the pH value of the resin was reduced. Wood also reduced the activation energies for both the core and face resins by decreasing the pH values of the curing systems. Moreover, the effects of wood on the curing behavior of the resins among the five species studied were similar. The lowest activation energy for a phenolic resin probably appeared at pH 10–11 under alkaline conditions. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 185–192, 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".