Sustainable Leaching of Cu, Ni, and Au from Waste Printed Circuit Boards Using Choline Chloride-Based Deep Eutectic Solvents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electronic waste (e-waste) is becoming a serious problem that impacts the environment due to its fast-growing volume. This rise is linked to high electronic and electrical equipment production to meet the increasing demand for high-end electronic devices. Conventional e-waste recycling approaches, including hydrometallurgy and pyrometallurgy, often involve substantial water and energy consumption and the generation of by-products, such as the emission of toxic gases or hazardous effluents. Within this context, solvometallurgy has emerged as a compelling alternative, whereby green non-toxic non-aqueous solvents, namely deep eutectic solvents (DESs), are used to extract and recover the metals with minimal water and harsh acid/base chemical use. The current study presents the solvo-leaching results of critical and strategic metals, i.e., copper (Cu) and nickel (Ni), and precious metals, i.e., gold (Au), from waste printed circuit boards (PCBs). Five different DESs were tested at mild conditions, namely at a temperature of 65 °C, a stirring speed of 300 rpm, a solid/liquid ratio of 10 g/L, and in the presence of iodine (I2) for 96 h. Among the different solvents tested, the one consisting of choline chloride (ChCl), acetic acid (AA), and I2 emerged as the optimal solvent, leading to the selective extraction of 99% of Cu, 92% of Ni, and 90% of Au from the PCB powder.
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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 | 0.000 |
| 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) | 0.000 | 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 it