Innovative Application of Microwave Treatment for Recovering of Rare Earth Elements from Phosphogypsum
Bibliographic record
Abstract
Some rare earth elements (REEs) are classified as strategic materials because of their increasingly high demand, supply uncertainty, and near zero recycling. For tackling the sustainability challenges associated with REEs, their technospheric mining, i.e., recovery from secondary sources, is imperative. Characterization results indicate that phosphogypsum, a byproduct of the fertilizer industry, contains about 0.03–0.4 wt % REEs. Here, a novel process was developed that utilizes microwave irradiation to enhance the leaching efficiency of REEs from phosphogypsum. Optimal REE leaching was achieved by either microwaving at low power (600 W) and short duration (5 min) or at high power (1200 W) and long duration (15 min). The former creates cracks and pores in the particles, enhancing the infiltration of lixiviant, with minimal conversion of gypsum into less soluble crystals. The latter results in thermal degradation of the PG particles and the release of REEs at the cost of changing the PG crystal structure to less soluble phases. In all cases microwave pretreatment had a positive effect (more than 20% increase) on REE leaching efficiency. At the optimum microwaving conditions [15 min irradiation (2.45 GHz) at 1200 W], 80% Nd, 99% Y, and 99% Dy leaching efficiency was achieved.
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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".