Influence of basic catalyst and organosilane types on dimensional stability and durability of exterior wood cladding
Bibliographic record
Abstract
To extend service life in exterior cladding applications, wood must be treated and/or coated. White spruce (Picea glauca (Moench) Voss) is a common wood species used in Canada for exterior cladding applications. White spruce is a refractory species and can be hard to treat with conventional approaches. Organosilanes could be an interesting wood treatment as condensation between organosilanes and wood hydroxyls can occur leading to a decrease in hydroxyl content, which are responsible for the dimensional instability of the wood. White spruce was pressure-treated with different formulations of organosilanes using acid and basic catalysts as well as hydrolysed and non-hydrolysed organosilanes. Weight percent gains were quite low (up to 11%) due to white spruce anatomy, and also because the organosilanes were located only in the cell wall. White spruce treated with organosilanes showed the best anti-swelling efficacy (up to 30%) for a non-hydrolysed formulation using an acid catalyst and the smallest organosilane molecule. The basic catalyst did not improve the anti-swelling efficacy compared to the acid catalyst because of its low mass gain. Three types of commercial coatings were applied on the treated wood and untreated control. The coated samples were submitted to accelerated UV exposure for 1,500 h to evaluate colour change and adhesion. The opaque stain exhibited the smallest total colour change (ΔEab*) with or without the modification of organosilanes.
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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".