Surface characteristics of selected wood species after treatment with tannin and ammonia vapor
Bibliographic record
Abstract
Effects of ammonia vapor and tannin treatments were studied relative to the properties of wood. The color change, surface roughness, and surface hydrophobicity of Persian oak (Quercus persica), Persian walnut (Juglans regia L.), Oriental beech (Fagus orientalis Lipsky), and Siberian pine (Pinus sibirica) were evaluated after treatments for 8 and 24 h. The color difference (ΔE*) values increased with prolonged exposure, with the highest changes observed in tannin-treated samples exposed to ammonia vapor for 24 h. Pronounced color changes were observed in Siberian pine samples, while beech and oak showed moderate color shifts. Walnut exhibited a more complex response, with an initial increase in yellowness followed by stabilization. Surface roughness measurements demonstrated a significant increase, particularly in maximum height (Rz), indicating substantial modifications to the wood surface. The most significant increase in roughness was observed in the samples treated with ammonia vapor and tannin after 24 hours of exposure, regardless of species type, although oak and walnut showed more controlled alterations. The surface hydrophobicity of the samples was increased after treatment, with the highest contact angle values after treatment for 24 h. This study highlights the potential of tannin and ammonia vapor treatments for improving the aesthetical and surface properties of wood.
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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".