Leaching and recovery of rare earth elements, copper, nickel, silver and gold from used smartphone circuit boards
Bibliographic record
Abstract
Printed circuit board (PCB) assemblies constitute a concentrated source of valuable metals. This study evaluates the performance of a complete hydrometallurgical process for extracting and recovering rare earth elements (REE), Cu, Ni, Ag and Au from leachates produced from PCB found in smartphones via four selective leaching steps. In a REE leachate ([Dy] = 43 mg/L, [Gd] = 5 mg/L, [Nd] = 266 mg/L, [Sm] = 35 mg/L, [Tb] = 8 mg/L, [Ho] = 2 mg/L), 92 % of REE was precipitated at room temperature with H 2 C 2 O 4 /REE molar ratio of 2/1. Calcination of the REE-oxalate precipitates at 800 °C resulted in a mixture of rare earth oxides (REO) with a 91 % purity. From the base metal leachate ([Cu] = 19,376 mg/L and [Ni] = 1,264 mg/L), Cu was electrodeposited during 120 min (pH = 3, current 270 A/m 2 ) while Ni was precipitated by addition of oxalic acid (H 2 C 2 O 4 /Ni molar ratio of 2/1, pH 4.4, T = 60 °C, t = 60 min), followed by calcination at 600 °C for 4 h to form NiO (93 % purity). Three oxidative leaching steps (10 % w/v solids, T = 80 °C, t = 180 min, 1.0 M H 2 SO 4 , 67 g H 2 O 2 /L, T = 80 °C, t = 180 min) solubilized 97 % of Ag. Subsequently, with the addition of Cu (Cu/Ag mass ratio of 2), at room temperature and 120 min, Ag was precipitated 99.4 % in the first leachate ([Ag] = 488 mg/L). A Zn/Au mass ratio of 30 precipitated 99.1 % of gold at the room-temperature from the gold leachate ([Au] = 107 mg/L).
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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 | 0.000 |
| 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 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".