Deactivation of chromated copper arsenate as a catalyst in smouldering of wood
Bibliographic record
Abstract
Chromated copper arsenate (CCA) is a preservative treatment that enhances the biodegradation resistance of wood, essential for prolonging the service life of exterior infrastructure. However, the susceptibility of CCA-treated wood to smouldering combustion presents a significant challenge, as the metals present in the CCA catalyze the smouldering. In this work, we examined the oxidative behaviour of char produced from CCA-treated wood through dynamic thermogravimetric analysis. There was a gradual decrease in the catalytic activity of the CCA as temperature increased, particularly above 400 °C. At this stage, lignin undergoes secondary pyrolysis, and thermal decomposition of CCA complexes occurs. The thermal decomposition of CCA-treated wood at temperatures above 650 °C was similar to that of untreated wood, indicating the possible deactivation of the CCA. The agglomeration of species containing Cu or Cr above 650 °C might be responsible for the deactivation. This process is influenced by simultaneous lignin pyrolysis and decomposition of CCA complexes, which are also likely contributors to the loss of CCA's catalytic activity. This research introduced a novel experimental approach to assess the catalytic effects of CCA on char oxidation at elevated temperatures, offering valuable insights into CCA deactivation and its implications for fire safety. It also contributes to the development of potential modifications to CCA formulations aimed to reduce smouldering in wildfire-prone regions. ● CCA catalyst deactivation was observed, completing near 650 ℃. ● CCA did not change char composition or pyrolysis pathway; elevated temperatures did. ● Cu and Cr agglomeration above 650 ℃ may lead to CCA deactivation. ● Lignin pyrolysis and CCA decomposition may also contribute to catalytic loss.
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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".