Bioleaching of rare earth elements (REEs) from Indonesian red mud by the bacterium <i>Bacillus nitratireducens</i> strain SKC/L-2
Bibliographic record
Abstract
Red mud, a residue of the bauxite industry, represents a secondary source of rare earth elements (REEs) with substantial commercial value and untapped potential. Bioleaching, an innovative, cost-effective, and environmentally friendly method, offers a means of extracting valuable metals from mining wastes. This study explored the bioleaching of Indonesian red mud using Bacillus nitratireducens strain SKC/L-2 to recover REEs. The experiments were carried out over three days at 25 °C with different concentrations of red mud (1.5, 3, and 6 g/L) and a 10% v/v bacterial inoculum in a specific bioleaching medium. The findings indicated a slight reduction in REEs extraction by the bacterium as the red mud concentration increased from 1.5 to 6 g/L in the direct bioleaching process. In the experiment using 1.5 g/L red mud, 16 REEs were successfully extracted, with high extraction levels for specific elements such as Lu (92.0%), Tb (80.61%), and Gd (67.42%). However, when the red mud concentration was increased to 6 g/L, the survival potential of Bacillus nitratireducens strain SKC/L-2 decreased, leading to reduced recovery of elements such as Lu (76.80%), Tb (70.30%), and Gd (55.83%). The study reveals the behaviour of Bacillus nitratireducens in interacting with red mud and enduring high alkalinity, resulting in REEs extraction. These findings enhance the understanding of microbial interactions with red mud and provide insights into potential resource recovery applications.
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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) | 0.001 | 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) | 0.000 | 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".