Genetic characterization, nickel tolerance, biosorption, kinetics, and uptake mechanism of a bacterium isolated from electroplating industrial effluent
Bibliographic record
Abstract
Electroplating industries in Madurai city produce approximately 49,000 L of wastewater and 1200 L of sludge every day revealing 687-5569 ppm of nickel (Ni) with other contaminants. Seventeen Ni-tolerant bacterial strains were isolated from nutrient-enriched effluents. Among them one hyper Ni accumulating strain was scored and identified as Bacillus cereus VP17 on the basis of morphology, biochemical tests, 16S rDNA gene sequencing, and phylogenetic analysis. Equilibrium data of Ni(II) ions using the bacterium as sorbent at isothermal conditions (37 °C) and pH 6 were best adjusted by Langmuir (R(2) = 0.6268) and Freundlich models (R(2) = 0.9505). Experimental validation reveals Ni sorption takes place on a heterogeneous surface of the biosorbent, and predicted metal sorption capacity is 434 ppm. The pseudo-second-order kinetic model fitted the biosorption kinetic data better than the pseudo-first-order kinetic model (R(2) = 0.9963 and 0.3625). Scanning electron microscopy, energy dispersive X-ray, and Fourier transform infrared spectroscopy studies of the bacterial strain with and without Ni(II) ion reveals the biosorption mechanism. The results conclude possibilities of using B. cereus VP17 for Ni bioremediation.
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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".