Removal of Chromium(VI) from Contaminated Water Using Untreated Moringa Leaves as Biosorbent
Bibliographic record
Abstract
Biosorption of chromium (Cr(VI)) is studied by using raw (chemically not modified) Moringa (Moringa Oleifera) leaf powder without any pretreatment. Cr(VI) is one of the potentially harmful heavy metals found in industrial wastewater. In the Moringa leaf powder, the presence of a significant amount of organic acids form the source for the biosorption of Cr(VI). The concentration of Cr(VI) in the feed solution is varied and different dosages of the proposed biosorbent are used to study its efficiency in the removal of Cr(VI). The concentration of Cr(VI) is varied from 1 ppm to 20 ppm while the amount of biosorbent is varied from 0.5 g to 2.5 g. The equilibrium time for adsorption of Cr(VI) is observed to vary between half an hour and 90 min. The metal removal efficiency varied from 30% to 90% which is a significant achievement compared to other conventional methods which are either energy-intensive or not cost effective. The experimental results are modeled using Langmuir, Freundlich and Redlich–Peterson isotherms. The metal removal efficiency is attributed to the chelating effect of carboxylate and hydroxyl groups present in the moringa leaves and is confirmed from the FTIR analysis. Further molecular docking simulations are performed to confirm the binding of the metal to the speculated sites within the different acids present in the moringa leaves. Untreated green moringa leaf powder used as a biosorbent in this study leads to a sustainable and cheaper option for treating wastewater containing Cr(VI).
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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".