On the Investigation of the Thermal Degradation of Waste Printed Circuit Boards for Recycling Applications
Bibliographic record
Abstract
Abstract Electronic products contain a wide range of materials including pure metals and alloys, ceramics, and polymers. Disassembling each individual component of such products to recycle is impossible. For that reason, end‐of‐life electronic products like waste printed circuit boards (WPCB) are shredded and sent to pyrometallurgical processes to recover valuable metals. One major issue with recycling is the release of gaseous brominated species that need to be captured or stabilized. It is crucial to have a fundamental understanding of the chemical interactions that occur between each component of the WPCB in these processes. The thermal degradation mechanism of WPCB is investigated here using differential scanning calorimetry coupled to thermogravimetric analysis of both synthetic samples and shredded WPCB. Computational thermochemistry is used to support the identified reaction mechanisms which involve the release of gaseous hydrogen bromide evolving from Tetrabromobisphenol A used as a flame retardant. The chemical reactivity between this compound and each major WPCB component is quantified. It is proven that HBr(g) reacts with copper, iron, and CaO to form bromides which stay in the solid pyrolysis residue up to a temperature of 580 °C. Above this temperature, CuBr2 and FeBr2 start to evaporate and are lost in the gas phase.
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) | 0.000 | 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".