Effect of ultrasound-assisted pretreatment on the hygroscopic dimensional stability of Ailanthus altissima wood modified with 1,3-dimethylol-4,5-dihydroxy ethylene urea
Bibliographic record
Abstract
Abstract Fast-growingwood is particularly favored as the lightweight material and has potential used in construction and furniture. However, dimensional instability, one of fast-growing wood’s inherent weaknesses, limits its utilization as solid wood products. In this study, fast-growing Ailanthus altissimawood was modified with 1,3-dimethylol-4,5-dihydroxy ethylene urea (DMDHEU) after ultrasound-assisted pretreatment to improve its hygroscopic dimensional stability. Ultrasound was first used in combination with DMDHEU for wood impregnation modification. The weight percentage gain (WPG), absolute dry density, tangential/radial swelling rates, resin distribution and functional groups of chemical components were investigated. The results indicated that the WPG and absolute dry density of DMDHEU-ultrasonic samples increased compared to DMDHEU-untreated samples. The hygroscopic dimensional stability of DMDHEU-ultrasonic samples improved to varying degrees compared to DMDHEU-untreated samples. In particular, the tangential/radial swelling rates of DMDHEU-ultrasonic-4% NaOH samples decreased from 2.01%/1.22% for untreated samples to 0.55%/0.38%. On the one hand, ultrasound-assisted pretreatment affected the distribution of resin inside vessel. Raised-membranous resin appeared along the vessel of DMDHEU-ultrasonic-4% NaOH samples, while only sporadic resin deposition was observed for the DMDHEU-untreated samples. On the other hand, ultrasound-assisted pretreatment promoted better penetration of the DMDHEU resin into the wood cell wall, and the hydroxyl groups in the hydrophilic part were converted into hydrophobic ester bonds and ether bonds. In conclusion, ultrasound-assisted pretreatment had a positive effect on the wood DMDHEU impregnation. And these findings shed light on promising pretreatment methods for wood dimensional stability modification.
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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".