Removal of hexavalent chromium in tannery wastewater by <i>Bacillus cereus</i>
Bibliographic record
Abstract
Bacillus cereus was used to remove chromium (Cr(VI)) from medium containing tannery wastewater under different conditions. The maximum rate of Cr(VI) removal was attained at a temperature of 37 °C, pH of 7.0-9.0, and biomass of 20 g/L when the initial Cr(VI) concentration was less than 50 mg/L. Under the optimum conditions, the Cr(VI) in tannery wastewater was treated with each cellular component of B. cereus to detect its ability to reduce Cr(VI). The results showed that the removal rate of Cr(VI) for the cell-free extracts could reach 92.70%, which was close to that of the whole cells (96.85%), indicating that the Cr(VI) reductase generated by B. cereus is primarily intracellular. Additionally, during continuous culture of the B. cereus, the strain showed good consecutive growth and removal ability. After treatment of 20 mg/L Cr(VI) for 48 h, the B. cereus was observed by SEM and TEM-EDX. SEM images showed that the B. cereus used to treat Cr(VI) grew well and had a uniform cellular size. TEM-EDX analysis revealed large quantities of chromium in the B. cereus cells used to treat Cr(VI). Overall, the results presented herein demonstrate that B. cereus can be used as a new biomaterial to remove Cr(VI) from tannery wastewater.
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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 | 0.000 | 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".