Recyclability and recovery of carbon from waste printed circuit boards within a circular economy perspective: A review
Bibliographic record
Abstract
Waste printed circuit boards (WPCBs) are a significant component of electronic waste (e-waste) and are among the fastest-generating waste flows. The potentially negative impacts caused by e-waste on the environment and human health pose an increasingly apparent threat to people's everyday lives and well-being. The nonmetallic fraction (predominantly carbon) of WPCBs is characterized by heavy weight, low resource value, and complex composition, and these characteristics significantly restrict the recycling of the WPCBs to achieve a circular economy. To bring more attention and better guidance to carbon recycling in printed circuit boards, this study utilizes a recyclability model to analyze the potential carbon recycling in WPCBs. It also utilizes existing life cycle assessment results to evaluate the carbon emissions of WPCBs in waste management systems and to identify potential opportunities for carbon recovery within the entire system. In addition, this study reviews the latest technological advances in recovering carbon from WPCBs, including separation, oxidation, and activation. The properties of recycled carbon, such as porosity, adsorption, and electrochemical characteristics, are also a key focus of this review. The application of recycled carbon plays a crucial role in shaping the future direction of e-waste carbon recycling and its potential contribution to achieving a circular economy. The research results are expected to guide future carbon recycling processes and provide a reference for industrial development. • Quantitative analysis is essential to achieving effective e-waste recycling. • The separation of metal and non-metal fractions improves the recycling of WPCBs. • The disposal stage plays a crucial role in the carbon footprint of PCB life cycles. • Pyrolysis activation is popular for creating hierarchical porous carbon from WPCBs. • Recovered carbon from WPCBs can produce catalysts and energy storage devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".