Ageing of modified wood. Part 1: Wetting properties of acetylated, furfurylated, and thermally modified wood
Bibliographic record
Abstract
Abstract The main objective of this work was to apply contact angle analysis to predict work of adhesion (W a ) between some modified wood materials and certain thermoplastics and adhesives. Wetting properties, i.e., contact angles, were measured by the Wilhelmy method on both freshly prepared and aged veneer samples of unmodified and acetylated Scots pine, furfurylated radiata pine, and heat treated Norway spruce. The sessile drop method was used to measure contact angles on a phenol resorcinol formaldehyde, an emulsion polymer isocyanate, and a one-component polyurethane adhesive. Contact angle data were also collected from the literature on polyethylene, polyvinyl chloride, polymethyl methacrylate, polystyrene, and Nylon 6. Contact angle analysis based on the Chang-Qin-Chen model was then applied to determine so-called acid-base interaction parameters and W a between the wood samples and the selected thermoplastics and adhesives. Results show that the ageing process led to an increased hydrophobic character of unmodified, heat treated, and furfurylated wood samples. The freshly prepared acetylated wood samples had a pronounced hydrophobic character which remained approximately constant after ageing. The predicted W a between the wood and the adhesives was considerably higher than that between the wood and the thermoplastics. Furthermore, the predicted W a between the acetylated wood and both the thermoplastics and water was approximately unchanged when comparing the fresh and aged samples. In contrast, the ageing of all other wood samples resulted in a dramatic decrease of the wood-water W a and a moderate decrease of the wood-thermoplastics W a . The wood-adhesives W a , however, was unchanged for the unmodified and furfurylated wood when comparing the fresh and aged samples and even increased for heat treated and acetylated wood samples.
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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".