Functionalized Surface Layer on Poplar Wood Fabricated by Fire Retardant and Thermal Densification. Part 2: Dynamic Wettability and Bonding Strength
Bibliographic record
Abstract
In continuation of our former study on a novel combined treatment of nitrogen–phosphorus fire retardant and thermomechanical densification on wood, this study focuses on the dynamic wettability and the bonding strength. The contact angle was measured using the sessile drop method and the surface energy was calculated according to the van Oss method. Water surface penetrating and spreading is analyzed by both the Shi and Gardner model and the droplet volume changing model. The results reveal that the combined treatment increased the surface energy, especially the acid–base component. The contact angle declined and the water droplet spread more easily on the surface. Meanwhile, the rate of relative droplet volume decreased by 32.6% because the surface layer was densified and stabilized by the combined process. Additionally, the surface possesses the lowest roughness and highest abrasion resistance on the tangential section. Thus, the bonding strength of the combined treated poplar decreased by 29.7% compared to that of untreated poplar; however, it is still 53.3% higher than that of 220 °C heat-treated wood.
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".