Column Bioleaching of Nickel from Sulfidic Samples with Different Nickel and Magnesium Content
Bibliographic record
Abstract
Nickel is a valuable metal that is becoming more prevalent in the industry. Column bioleaching was used in this study to extract nickel from magnesium-bearing sulfide minerals. Two different sulfidic samples with different nickel and magnesium content were utilized to investigate the performance of column bioleaching. It was discovered that mesophilic cultures’ adaptation is delayed by increasing magnesium contents. Bioleaching outperformed leaching in terms of recovery by 80% compared to 50% in sample 1 and 70% compared to 40% in sample 2. Jarosite is precipitated in samples with a high magnesium content due to the high pH and oxidation level, which lowers bioleaching effectiveness. The pretreatment method using acid washing before the start of bioleaching treatment can reduce the amount of magnesium in samples, which increases the Ni recovery in both samples. SEM analysis was performed on each bioleaching residue. The result showed that high amounts of magnesium in the second sample could be a factor in the precipitation of jarosite. Finally, it can be concluded that the pretreatment method is a feasible Bio-heap operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".