Effect of heat treatment on bonding performance of poplar via an insight into dynamic wettability and surface strength transition from outer to inner layers
Bibliographic record
Abstract
Abstract Heat treatment (HT) is thought to degrade wood surface wettability and cause gluing problems; this study focused on wettability and surface strength of the surface layer on heat-treated wood. The outer and inner surfaces formed by removing the 1-, 2- and 3-mm surface layers of heat-treated poplar on the tangential section were investigated. Dynamic wetting was analyzed according to the sessile drop method. The bonding failure models on different surfaces were also discussed based on both images of macro- and microscopic fracture interfaces. Using Fourier-transform near-infrared spectroscopy (FT-NIR), the cell wall chemistry on both outer and inner surfaces were analyzed. The results showed that the bonding strength of the outer surface was mainly affected by wettability, whereas the surface strength became the key factor for the inner layers. The removal of the first 1 mm of the surface layer enhanced the wetting process and transferred the failure mode from the glue line to the wood itself. FT-NIR revealed that the intensity of the thermal degradation on inner layers was alleviated with the removal depth; wettability and surface strength were enhanced compared with the outer surface. Surface abrasion and hardness declined, decreasing the surface strength and bonding capacity. This study indicates that the bonding of heat-treated wood is truly affected by the surface strength of the inner layers, in addition to the wettability on the outer surface.
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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".