Microbial Sorption Studies for Removal of Trivalent Chromium from Model Tanning Bath
Bibliographic record
Abstract
Out of the industrial waste streams/effluents entering in the aquatic system containing metallic species, tanneries release high amounts of chromium, an anthropogenic pollutant because of use of basic chromium sulphate in the tanning processes. Trivalent chromium, Cr (III) is the targeted ionic species for removal by biosorption on a fungal species in this work, as the technique has inherent merit of easy adsorbent regeneration and lower capital costs. The study involves the use ofAspergillus niger(A. niger), to remediate chromium from a model tanning bath with Cr (III) concentration of 500 mg L-1. The fungal species was grown in Czapek Dox media at pH 2.5 and 35°C temperature and its biomass was used in various forms such as live, autoclaved and alkali treated. With 1% (w/v) alkali treated biomass, the biosorption of chromium reached a maximum of 91% for a feed concentration of 500 mg L-1in 2 h time at pH 2.5, temp 35°C and A/R (adsorbent : solution volume) ratio of 1/100. The lower biosorption of metal (42 - 44%) was observed with live and autoclaved biomass. The biosorption of chromium (III) on the fungal biomass was explained with various isotherms and fitted to the kinetic model involving first order expression. The study focuses on establishing the mechanism of bioremediation of chromium onA. niger.
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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) | 0.001 | 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".