Spent-medium leaching of germanium, vanadium and lithium from coal fly ash with biogenic carboxylic acids and comparison with chemical leaching
Bibliographic record
Abstract
Coal fly ash (CFA), produced in coal-fired power plants, is categorized as hazardous waste and has caused serious environmental impacts. The CFA contains significant amounts of critical metals and can be considered as a secondary resource for these metals. Therefore, through an appropriate recycling process, reducing CFA stocks helps to reduce its severe environmental impacts and to provide a potential metal resource. In the current research, a spent-medium bioleaching process with Pseudomonas putida and Pseudomonas koreensis was introduced for recovering germanium , vanadium, and lithium from CFA. For this purpose, organic acids were produced with the mentioned microorganisms and used for leaching experiments after salt roasting CFA with Na 2 CO 3 . The effect of different parameters has been investigated for CFA leaching in the presence of biogenic acids. The results showed that the highest recoveries of metals were obtained for the leaching test with organic acids produced by Ps. putida , at 500 rpm agitation speed, 3% pulp density and 75 °C. The Ge, V, and Li recoveries at optimum conditions were 83%, 98% and 97%, respectively. Kinetic modeling was employed to determine the effect of different parameters on the leaching efficiency. The results showed that reagent diffusion to the surface of the particles was a rate-limiting step in the leaching process. The activation energies for Ge, V and Li leaching, were 37.1 kJ/mol, 28.6 kJ/mol and 15.7 kJ/mol, respectively.
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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) | 0.000 | 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".