Controlling Selectivity of Surface Electro-Precipitation (SEP) in the Recovery of Rare Earth Elements (REE) from Aqueous Feedstocks
Bibliographic record
Abstract
SEP is an emerging green separation technique for the recovery of REE and other valuable elements from unconventional feedstocks. Its industrial adoption requires comprehensive mechanistic knowledge of its selectivity for REE vs typical background cations to achieve the desired separation. To bridge this gap, we experimentally studied SEP of neodymium Nd in chloride, nitrate, and sulfate solutions, in the absence and presence of calcium, aluminum, iron, zinc, and cobalt. We found that SEP is nonselective in the mass-transfer regime. It becomes selective in the mixed regime, with higher purification factors for elements with larger gaps in precipitation pH. At the same potential and initial pH, the selectivity of SEP in the mixed regime is controlled by the current (OH – generation rate) and background ions. In the case of Fe, it additionally depends on the catalytic activity of the SEP cathode in the production of H 2 O 2 . We demonstrated for the first time that the in situ production of hydrogen peroxide in SEP can be used to selectively remove Fe from a multielement solution. The reported selectivity-recovery figures make SEP highly competitive, especially when its other advantages are factored in. The results of this study can be the basis for developing a suitable SEP-based strategy for preconcentrating REE and other valuable elements from diluted secondary resources.
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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 | 0.000 | 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".