A dual resin application system for improved bamboo-wood bonding
Bibliographic record
Abstract
In this work, a dual resin application system using commercial phenol formaldehyde (PF) resins with different molecular weight (MW) was investigated to improve bonding performance of bamboo and wood composite laminates. Water droplet contact angle was deemed to be unreliable for assessing resin wettability on bamboo due to its unique tissue structure compared with wood. Microscopic observation of the resin penetration showed high MW PF largely remained in the glueline and only entered the lumens of cut or damaged bamboo cells near the bondline. Low MW PF appeared in cell corners of bamboo parenchyma but not lumens. Applying low MW PF to the bamboo and high MW PF to the wood surface separately significantly improved bond shear strength with reduced difference between dry and wet conditions. The dry and wet bond strengths using the new method were enhanced by 36.5 % and 97.4 %, respectively, compared to high MW PF alone. The results suggest that low MW PF can permeate bamboo cell walls and fortify them against swelling and stress on the bamboo-resin interface in wet conditions. Further modifications are required to produce a stronger adhesive than the bamboo tissue to improve wet shear fiber failure rates and develop a viable structural bond qualification test for bamboo and bamboo-wood composites.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".