Recycling Particleboard by Acid Hydrolysis: Effects on the Physical, Thermal, and Chemical Characteristics of Recycled Wood Particles
Bibliographic record
Abstract
Acid hydrolysis can be more efficient than water hydrolysis, particularly in breaking down cured adhesives found in waste panels within a shorter reaction time that could benefit large-scale industrial processes. This study evaluates the effects of various acid hydrolysis conditions on the thermal, physical, and chemical properties of recycled particles intended for particleboard production. Particleboards were recycled using oxalic acid and ammonium chloride at different concentrations and reaction times at 122 °C. The thermal stability of the particles was determined by thermogravimetric analysis. Particle size distribution, particle morphology, nitrogen content, pH and acid/base buffer capacity were analyzed. The effect of the recycled particles on the urea-formaldehyde (UF) curing was assessed using differential scanning calorimetry and the gel time method. The recycled particles exhibited a higher thermal degradation beyond 200 °C, indicating their thermal stability for manufacturing new panels. The acid treatments did not damage the anatomical structure of the particles. The nitrogen content of recycled particles decreased by up to 90% when oxalic acid was used, compared to raw board particles. Recycled particles had a lower pH, a lower acid buffer capacity, and a higher base buffer capacity than those of raw board particles. The recycled particles did not significantly affect the peak polymerization temperature of the UF adhesive. However, some treatments affected the gel time of the adhesive. The results indicate that particleboards can be effectively recycled through acid hydrolysis, mainly with oxalic acid, which gives better results than hydrolysis using water alone. Oxalic acid showed increased selectivity in eliminating the cured UF adhesive, resulting in recycled particles suitable for manufacturing new panels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".