DSC characterisation of urea‐formaldehyde (UF) resin curing
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effects of various catalyst contents, resin solid contents, catalyst species and wood extract on urea‐formaldehyde (UF) curing by differential scanning calorimetry (DSC) technique. The finding obtained would benefit the manufacturers of UF‐bonded composite panels. Design/methodology/approach The UF curing rate under each condition in terms of DSC peak temperature was measured by high‐pressure DSC at a heating rate of 15°C/min; the correlations of peak temperature with catalyst content, resin solid content, catalyst species and wood extract, respectively, were regressed via a model equation, which described the curing characteristics of the UF bonding system. Findings A model equation, Tp=A · EXP(−B · CC per cent)+D, was proposed to characterise the DSC peak temperatures or the rate of UF curing with regressing coefficients greater than 0.97 (commonly greater than 0.99). The constants A and B in the model equation were found to correspond to kinetic characteristics of UF resin curing reaction. The constant D in the model equation is believed to be associated with the utmost peak temperature, which implies that the DSC peak temperature will finally reach a maximum with catalyst content increasing. It was also found that the wood extracts having higher pH value and base buffer capacity had stronger catalyses on UF curing. Research limitations/implications The catalysts commonly used in medium density fibreboard plants or particleboard plants are those having the utmost peak temperature of about 90‐95°C; the catalyses of wood extracts were much weaker than that of catalyst NH4Cl. Practical implications The model equation could be used to predict the peak temperature or the curing rate of UF resin, and to quantify the effects of wood extracts on UF curing. Originality/value The study developed a model equation that can well characterise the UF curing, and quantified the effects of wood extracts on UF curing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".