Advancing the circular economy: green recovery of rare earth elements (REEs) from coal combustion fly ash using biohydrometallurgical techniques with mixotrophic bacteria
Bibliographic record
Abstract
Coal remains a crucial energy source, with European countries reactivating coal-fired stations to prevent winter blackouts, highlighting its energy security importance. This research investigates coal fly ash (CFA) from a power plant in Indonesia, which is rich in rare earth elements (REEs) that are vital for contemporary technologies. The analysis reveals that the CFA is primarily composed of Si, Fe, Al and Ca, and is notably concentrated in REEs such as Ce, Y, La, Nd, Sc and Pr. Employing biohydrometallurgical techniques involving mixotrophic bacteria, this study aims to sustainably extract these REEs. The findings indicate effective recovery, especially of heavy REEs like terbium, achieving a maximum extraction rate of 70%. Six bacterial strains demonstrated enhanced efficacy in extracting heavy rather than light REEs. The research emphasises the potential of CFA as a significant secondary source of critical metals, promoting eco-friendly extraction methods as feasible and sustainable alternatives.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".