Thermo-hydrolytic recycling of urea-formaldehyde resin-bonded laminated particleboards
Bibliographic record
Abstract
The large global production of particleboards creates an equal quantity of particleboard waste after completion of their service life. Given increasing demand for green products and government environmental policies, it is urgent to develop technologies to recycle these used composite panels into valuable raw materials. This study was conducted to recover particles from waste laminated particleboards using various thermo-hydrolytic treatments. The recovered particles were used as raw materials with different substitutions of fresh particles to manufacture particleboard panels. The performance of the resulting particleboards was evaluated in terms of their mechanical properties and formaldehyde emissions. The nitrogen content of the control and resulting particles were measured to determine the resin removal in recycled particles. The results suggested that different thermo-hydrolytic treatments did not have significant influence on particles size distribution. Approximately 65% of urea-formaldehyde resin was removed from the particleboards treated at 140 °C/20 min. Particles recycled at 140 °C/20 min were comparable to fresh particles in terms of mechanical properties and formaldehyde emissions, and 100% of the recycled particles were used in the manufacture of particleboard without an adverse impact on the board performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".