Curing characteristics of urea–formaldehyde resin in the presence of various amounts of wood extracts and catalysts
Bibliographic record
Abstract
Abstract The characteristics of urea–formaldehyde (UF) resin curing in the presence of wood extracts and a catalyst [ammonium chloride (NH 4 Cl)] were investigated by differential scanning calorimetry (DSC). The effects of extracts from 16 wood species on resin curing behaviors were evaluated. A model developed in this study, T p = 53.296 exp(−9.72 C ) + 93.104, could be used to predict the resin curing rate in terms of the DSC peak temperature ( T p ) as influenced by the NH 4 Cl content ( C ). The results indicated that the curing rate of UF resin increased as the catalyst content increased and reached a maximum when the catalyst content ranged from 0.5 to 1.0% (solid basis over liquid UF resin weight). Further increases in the catalyst content had no effect on the resin curing rate. The curing rates of UF resin in the presence of wood extracts increased with decreased pH values or increased base buffer capacities. It was also discovered that the activation energy could not fully explain the resin curing behavior when some species of wood extracts were present, and therefore, the pre‐exponential factor had to be taken into account. The concept of the equivalent catalyst content (ECC) of wood extracts to the NH 4 Cl content was introduced in this study; ECCs ranged from 0.0022 to 0.0331% among the 16 wood species. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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".