Recovery of gold from e-waste via food waste byproducts
Bibliographic record
Abstract
Abstract Global materials’ and energy constraints and environmental issues call for a holistic approach to waste upcycling. We propose a chemically rational, cost-effective and environmentally friendly recovery of non-leaching gold from e-waste using aqueous chemistry with hydrogen peroxide, an environmentally benign oxidant, and lactic acid, a food chain byproduct. The oxidation of the base metals enables the release of gold in its metallic state in the form of flakes subsequently separated via filtration. Our main byproduct is a precursor of Cu 2 O, a relevant metal oxide for solar energy conversion applications. The recovered gold was characterized by scanning electron microscopy, energy dispersive spectroscopy and x-ray photoelectron spectroscopy to gain insight into the morphology of the flakes and their chemical composition. Furthermore, recovered gold was used to successfully fabricate the source and drain electrodes in organic field-effect transistors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".