Seeing Environmental Issues as a Source of Rare Earths
Bibliographic record
Abstract
Rare Earth Elements (REE) are one of the most strategical resources and have become the main interest of many developing and developed nations in the past seventy years, and its importance tend to increase further. New technologies demand these elements and some of them are critical, with high demand and limited supplies, amongst them are Y, Nd, Eu, Dy and Tb [1], In the state of Minas Gerais (Brazil), the mining industry plays a crucial role in the economy, whose main products are notably lead, zinc, gold, niobium, and copper. Furthermore, Minas Gerais has strategically important Rare Earth Element (REE) ores, whereas production is still incipient Acid Mine Drainage (AMD) is a continuous natural leaching process that may contain variable concentrations of REE, it occurs in some sites around the world [4]-[6]. One of these sites is located in a closed uranium mine in Caldas, Minas Gerais, Brazil, where the REE concentrations in AMD are about 130 mg L -1 [7], The AMD waters are treated with lime with a maximum flow rate of 300 m 3 h -1 , and the neutralization of the waters consumes about 12 t of lime per day and generates enormous amounts of precipitate The recovery of the RRE can yield in approximately 936 kg daily. Our research team is studying two ways of recovering the REE present in the AMD waters, using ionic resins and co-precipitation with iron, aluminum and manganese oxihydroxides. Results show that REE can be successfully recovered by both methods with high efficiency. Specifically, the use of a cationic resin in batch experiments can recover up to 90% of the REE present in the feed solution at pH = 1.3. The co-precipitation with aluminum and manganese oxihydroxides at pH = 8 can recover up to 95% of the REE present in a laboratory AMD, producing a solid phase with 14% of REE oxides. Further studies focus on optimizing the processes and on concentrating the REE after the recovery, specifically the elution of the resins and the leaching of the precipitates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".