Pyrolysis of Chromated Copper Arsenate-Treated Wood: Investigation of Temperature, Granulometry, Biochar Yield, and Metal Pathways
Bibliographic record
Abstract
Chromated copper arsenate-treated (cca) wood disposal faces environmental restrictions due to its toxicity, heavy metal leaching in storage sites, and greenhouse gas emissions during incineration. Thus, finding new management methods for this contaminated wood at the end of life is crucial. This study evaluated the effect of pyrolysis temperature (300, 400, and 500 °C), particle size, biochar yield, and the behavior of arsenic (As), chromium (Cr), and copper (Cu) during treated-wood pyrolysis. The highest biochar yield was obtained at 300 °C for fine particles. The biochar retention of heavy metals decreased with increasing pyrolysis temperature. At 300 °C, the highest biochar As, Cr, and Cu retentions were 76, 91, and 83%. At 500 °C, biochar only retained 43% of the As. Additionally, heavy metal leaching from the biochar exceeded the Environmental Protection Agency’s (EPA) maximum concentration limit of 5 mg/L. High-density polyethylene encapsulation of contaminated biochar reduced the leaching of As, Cr, and Cu by 96, 95, and 91%, respectively. Thus, combining pyrolysis and plastic encapsulation to produce a composite material could be a solution for reducing waste (conversion of CCA-wood into biochar) and for the safe disposal of contaminated 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".