How does ultrasound contribute to the migration of extractives inside Ailanthus altissima wood?
Bibliographic record
Abstract
Extractives have an impact on the processing and commercial value of wood. Ultrasound is an environmentally friendly technology commonly employed to reduce the extractive content and thus enhance the permeability of wood. This study aimed to understand the migration mechanism of extractives inside wood during ultrasonic treatment, which may help to obtain the desired wood properties. The extractive distribution of Ailanthus altissima was observed by using stereo microscopy, optical microscopy, and scanning electron microscopy, the extractive content was determined, and the relationship between the concentration of water-soluble extractives and absorbance was measured using a UV/Vis spectrophotometer, and the migration model of extractives was studied using layered extraction by innovatively combining the weight and the absorbance methods. The results revealed that the extractives were predominantly distributed in the vessels and diminished after ultrasonic treatment. The extractive content gradually decreased over time (0 ∼ 5 h), with a rapid decline observed within the first 2 h. The concentration of the water-soluble extractives exhibited a proportional relationship with the absorbance. Through the comparison of the layered-extractive concentration, accumulating evidence suggested that the migration of the extractives was a dynamic process, which included the extractives migrating towards easy-extracted area, moving along the direction of ultrasound propagation inside the wood, and leaching out of wood during ultrasonic treatment.
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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 | 0.001 |
| 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".