Biohydrometallurgical recovery of rare earth elements (REEs) from Indonesian red mud using the mixotrophic bacterium <i>Priestia aryabhattai</i>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The extraction of rare earth elements (REEs) from red mud, a by-product of alumina production from bauxite, poses considerable environmental and economic challenges. This study investigates the viability of bioleaching as a sustainable and environmentally friendly approach for REE recovery from red mud. Bioleaching employs microorganisms to extract valuable metals from ores and offers a potentially less harmful alternative to traditional chemical extraction techniques. Specifically, the objective of this study is to recover REEs from Indonesian red mud using the mixotrophic bacterium Priestia aryabhattai , which is capable of oxidizing both iron and sulfur and producing biosurfactants. The bioleaching experiments were carried out over a period of three days under aerobic conditions, with the introduction of a 10% v/v inoculum of P. aryabhattai . The experiments varied the concentrations of red mud in the bioleaching medium to 1.5, 3, and 6 g/L. The results indicated that the maximum recovery of heavy rare earth elements (HREEs) was approximately 70% for terbium (Tb), whereas the highest recovery of light rare earth elements (LREEs) was about 60% for gadolinium (Gd). Most notably, increasing the concentration of red mud resulted in lower REE recovery levels. In conclusion, this study demonstrates the effectiveness of biohydrometallurgical methods for REE recovery from Indonesian red mud. The findings support sustainable metallurgical practices and present a promising pathway for more environmentally responsible REE recovery.
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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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it