Improving dimensional stability of <i>Ailanthus altissima</i> wood by ultrasonic alkali-assisted DMDHEU treatment
Bibliographic record
Abstract
Abstract The effectiveness of DMDHEU (1,3-dimethylol-4,5-dihydroxy ethylene urea) modification is influenced by factors such as pore size distribution. In this study, ultrasonic-assisted-alkali pretreatment combined with DMDHEU impregnation was used to enhance the hygroscopic dimensional stability of Ailanthus altissima wood. The weight percentage gain (WPG), absolute dry density, tangential swelling, resin distribution, and chemical functional groups were analyzed. The results showed a marked reduction in the tangential swelling of DMDHEU-ultrasonic-4 % NaOH-pretreated samples, decreasing from about 2.0 % and 4.9 % in untreated samples (without any pretreatment or DMDHEU impregnation) to 0.6 % and 3.2 % at approximately 11.3 % and 75.3 % RH, Respectively. Compared to untreated samples, DMDHEU-ultrasonic-pretreated samples exhibited increased WPG, Absolute dry density, and dimensional stability. Ultrasound-assisted pretreatment also influenced resin distribution within vessels, with raised-membranous resin formations observed in the DMDHEU-ultrasonic-4 % NaOH-pretreated samples, in contrast to the sporadic deposition in untreated samples. Furthermore, XPS and ATR-FTIR results indicated ultrasonic pretreatment facilitated deeper DMDHEU resin penetration into the wood cell wall, converting hydrophilic hydroxyl groups into hydrophobic ester and ether bonds. These findings underscore the significance of ultrasonic alkali pretreatment in enhancing the efficacy of DMDHEU modification for A. altissima , contributing to improved durability and expanding potential applications for this fast-growing timber species.
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 | 0.000 |
| 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".