Effects of Nanofillers on Water Resistance and Dimensional Stability of Solid Wood Modified by Melamine-Urea-Formaldehyde Resin
Bibliographic record
Abstract
The water absorption and dimensional stability of wood impregnated with melamine-ureaformaldehyde (MUF) and wood impregnated with different nanofiller/MUF formulations were investigated. Three kinds of nanoparticles, Cloisite® 30B, Claytone® APA, and Cloisite® Na+, were selected and mixed with MUF resin, and subsequently impregnated into solid aspen wood through a vacuum and pressure process. The wood polymer nanocomposites were prepared by in situ condensation polymerization of the impregnated wood under specific conditions. Significant improvements in water repellency and better dimensional stabilities were obtained for the nanofiller/MUF-treated wood. The untreated wood absorbed around 63% of moisture after 24 h soaking in water, while water uptake was about 125% after 1 week immersion in water. The MUF resin-impregnated wood absorbed about 8.3% and 38.5% of moisture after 24 h and 1 week immersion in water, respectively. For the organophilic nanoclay/MUF resin-impregnated wood, much lower water absorption in the amounts of around 5% water uptake in 24 h and 22% after 1 week was observed. The antiswelling efficiency (ASE) was also improved from 63.3% to 125.6% for the nanofiller/MUF-treated wood. The significant improvement in water resistance and dimensional stability of the resulting wood polymer nanocomposites can be attributed to the introduction of MUF and nanofillers into the wood. X-ray fluorescence shows that some nanoparticles have migrated into the wood cell wall. Wood treatments with MUF and nanofiller/MUF showed no significant influence on the color of the wood, which is important for practical application of the treated wood in some specific areas such as flooring.
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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".