Peroxide Post-Treatment of Wood Impregnated with Micronized Basic Copper Carbonate
Bibliographic record
Abstract
Wood is vulnerable to significant color changes when used in exterior applications. Some copper-based wood preservatives use colorants to minimize this color change. This paper examines the ability of a peroxide post-treatment to turn wood impregnated with micronized basic copper carbonate (CuCO3·Cu(OH)2) (MBCC) a stable brown color. MBCC-treated wood, with and without peroxide post-treatment, along with associated controls were evaluated for color change, erosion and black-stain fungal resistance after exposure to artificial photo-degradation. The impact of the peroxide treatment on copper leaching was assessed in a laboratory experiment, and the impact on copper reactivity was assessed by electron parametric resonance (EPR) spectroscopy. Peroxide post-treatment of wood pressure impregnated with MBCC was shown to reduce color change by more than 50% compared to controls. Erosion due to photo-degradation and colonization by black-stain fungi were lower in samples treated with MBCC than in untreated controls and were relatively unaffected by peroxide post-treatment. The peroxide post-treatment was associated with increased amounts of mobile copper. This led to increased susceptibility to leaching and to a more than 60% increase in the amount of copper than had reacted with the 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) | 0.002 | 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".