Bioleaching of Metal-Contaminated Soil in Semicontinuous Reactor
Bibliographic record
Abstract
Recently, microbial leaching of heavy metals from contaminated soils with indigenous iron-oxidizing microflora has been successfully applied in flask experiments. However, the long hydraulic residence time (HRT) required and the batch mode are not well suited for an industrial scale process. Therefore, this research focused on bioleaching of Zn, Cu, and Mn from a contaminated soil in the semicontinuous mode. Metal leaching experiments were carried out in an 8 L semicontinuous stirred tank reactor (SCSTR) with a soil concentration of 100 g/L, as well as in 500 mL shake flasks. It was found that bioleaching in the SCSTR entails a reduction of treatment duration (from 10 to 2 days) and an increase in the solubilization efficiency. Metal solubilization efficiency in the SCSTR was 40% for Zn, 47% for Cu, and 34% for Mn. When the volume of the soil suspension remaining in the SCSTR between transfers was small (20% instead of 50%), corresponding to a shorter HRT (2.5 days instead of 4 days), the solubilization efficiencies were reduced. The volumetric oxygen transfer coefficient and the oxygen uptake rate were calculated in the SCSTR for both HRTs tested. The dissolved oxygen concentration below which the microflora does not grow was found to be 0.2 to 0.3 mg/L, and the concentration below which oxygen is limiting was 0.8 mg/L.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".