Biohydrometallurgical recovery of rare earth elements (REEs) from Indonesian red mud using the mixotrophic bacterium <i>Priestia aryabhattai</i>
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".