Thermal Cure Kinetics of Modified Cold-Setting Melamine-Urea-Formaldehyde Resins with Liquid Thickeners
Bibliographic record
Abstract
Cold-setting melamine-urea-formaldehyde (CS-MUF) resins being used as adhesives for manufacturing laminated wood timber products require to have a proper viscosity using thickener or filler. However, studies on thermal curing kinetics and behavior of the modified CS-MUF resins with liquid thickener are limited. Hence, this study focuses on the thermal cure kinetics of the modified CS-MUF resins with two liquid thickeners at three addition levels to obtain the resin viscosities of 4,000 mPa.s, 8,000 mPa.s and 12,000 mPa.s. Differential scanning calorimetry (DSC) was used to estimate cure kinetics of the modified CS-MUF resins using two analysis methods: 1) model-fitting (MFT) method containing the Kissinger (KSNG) analysis, and 2) model-free (MFK) methods containing Kissinger-Akahira-Sunose (KAS) analysis, and nonlinear isoconversional (VYA) analysis. The KSNG, KAS, and VYA analysis showed that all modified CS-MUF resins followed very similar curing behavior with small difference, except Resin #2 which followed the autocatalytic reaction model based on the Málek method. These results suggest that liquid thickeners increase to a proper viscosity of CS-MUF resins without major impact to their curing behavior.
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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".