Enhanced REE Sorption to MnO <sub>2</sub> with PO <sub>4</sub> and P <sub>2</sub> O <sub>7</sub> Ligands and Selective Desorption and Enrichment of Heavy REEs
Bibliographic record
Abstract
Acid mine drainage contains elevated levels of Mn(II) and critical minerals, including cobalt and rare earth elements (REEs). Even under acidic conditions, these metals can adsorb to the surface of MnO 2, which can be formed in situ via Mn(II) oxidation by permanganate. To lower reagent costs, we investigated conditions in which only a fraction of Mn(II) was oxidized. Additionally, phosphorous-bearing ligands (PO 4, P 2 O 7 ) were added to alter the surface chemistry of MnO 2, optimizing REE recovery. In stock solutions containing only REEs and MnSO 4, with PO 4 and P 2 O 7 ligand concentrations as low as 3 mM, adsorption to MnO 2 was significantly enhanced over that occurring in ligand-free solutions. MnO 2 surface charge was substantially more negative when ligands were present. For a synthetic acid mine drainage (SAMD) solution, where only 30% of the Mn 2+ was precipitated, adding these same ligands led to better or similar REE recovery as when all Mn 2+ was precipitated with excess KMnO 4 . In scaled-up experiments, the amount of KMnO 4 and NaOH used was decreased by up to 74 and 80%, respectively, while achieving similar or higher concentrations in the solid of both Co (24.4 mg of Co g –1 of solid) and REEs (7.07 mg of REE g –1 of solid). Furthermore, HREEs can be effectively and selectively desorbed from the surface of the MnO 2 by adding oxalate solutions or by adjusting the pH to 9–9.5 in the case of trials where P 2 O 7 was used to adsorb REEs to MnO 2 . This research promotes sustainable management of water resources and innovative recycling of waste, furthering the United Nation’s sustainable development goals, specifically regarding sustainable consumption, industry, and innovation.
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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 | 0.000 | 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) | 0.001 | 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".