Photocatalytic Materials Obtained from E-Waste Recycling: Review, Techniques, Critique, and Update
Bibliographic record
Abstract
Waste-derived materials obtained from the recovery and recycling of electronic waste (e-waste) such as batteries and printed circuit boards have attracted enormous attention from academia and industry in recent years, especially due to their eco-friendly nature and the massive increment in e-waste due to technological development. Several investigations in the literature have covered the advances achieved so far. Meanwhile, photocatalytic applications are especially of interest since they maintain mutual benefits and can be used for H2 production from solar water splitting based on semiconductor processing as a proper environmentally friendly technique for solar energy conversion. In addition, they can be utilized to degrade a variety of organic and non-organic contaminations. Nonetheless, to the best of the authors’ knowledge, there has not been any comprehensive review that has specifically been focused on e-waste-derived photocatalytic materials. In this regard, the present work is dedicated to thoroughly discussing the related mechanisms, strategies, and methods, as well as the various possible photocatalysts synthesized from e-wastes with some critiques in this field. This brief overview can introduce modern technologies and promising possibilities for e-waste valorization, photocatalytic processes, and new photocatalytic degradation methods of eco-friendly nature. This paper discusses various e-waste-obtained photocatalytic materials, synthesis procedures, and applications, as well as several types of e-waste, derived materials such as TiO2, ZnO, indium tin oxide, and a variety of sulfide- and ferrite-based photocatalytic materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".