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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".