Exploring tannin and caffeine for enhanced performance of oriented strand boards under varied conditions
Bibliographic record
Abstract
Abstract This study explores tannin and caffeine, natural compounds with inherent antifungal properties, as scalable treatments to enhance the physical and mechanical performance of oriented strand board (OSB). Unlike previous research that primarily focused on durability, this work evaluates the effects of these bio-based treatments on leaching resistance, internal bond strength (IB), bending properties, water absorption, thickness swelling, and vertical density profile (VDP). The results demonstrate significant improvements over commercially available OSB, particularly in mechanical performance. Post-leaching, both treatments exhibited superior IB, with tannin-treated panels achieving the highest IB value (0.27 MPa). The bending analysis revealed that caffeine-treated panels that were not subjected to leaching achieved the highest modulus of rupture (MOR, 62.54 MPa) and modulus of elasticity (MOE, 10.36 GPa). Both treatments retained significantly higher MOR and MOE values post-leaching compared to the industrial reference and untreated panels. Water absorption and thickness swelling were comparable among tannin-treated, caffeine-treated, and untreated panels but were significantly lower than those of the industrial reference, which was included for general benchmarking purposes alongside the laboratory-made control. VDP analysis showed caffeine-treated panels had the lowest density prior to leaching, whereas tannin-treated panels maintained the highest density after leaching. These findings highlight tannin and caffeine as effective and scalable treatments, providing a sustainable alternative as functional additives for industrial engineered wood product manufacturing.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".