Selective leaching of rare earths, base metals and precious metals from used smartphones
Bibliographic record
Abstract
Discarded smartphones represent a valuable source of rare earths (REE), base metals and precious metals. This study focussed on the optimisation of three-stage selective leaching conditions for REE, copper and precious metals (Au and Ag), respectively, contained in printed circuit boards (PCBs) found in end-of-life smartphones. The effects of several leaching conditions, such as sulphuric acid and thiourea concentrations, were investigated using a statistical approach based on a design of experiments using Box–Behnken methodology. Optimum leaching efficiencies were achieved when PCB powder was contacted (solid concentration of 100 g/L) with (1) a 0.2 M H2SO4 solution for 30 min at a temperature of 20°C for REEs; (2) a 1 M H2SO4 solution with 67 g H2O2/L for 180 min at 80°C for Cu and (3) a solution of 42 g thiourea/L in 0.1 M H2SO4 and 9 g Fe2(SO4)3/L for 120 min at 20°C for Au and Ag. Using these optimal conditions, a complete leaching procedure included an REE solubilisation step and a base metal leaching step, both repeated twice, and a precious metal leaching step. This procedure solubilised 91% of the REE, 100% of the copper, 98% of the gold and 87% of the silver contained in the PCB powder during their respective leaching stages.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".