Removal of Cr(VI) from wastewater by adsorption on iron nanoparticles
Bibliographic record
Abstract
Abstract Due to rapid industrialisation, the presence of heavy metals in water and wastewater is a matter of environmental concern. Though some of the metals are essential for our system but if present beyond their threshold limit value (TLV), they are harmful and their treatment prior to disposal becomes inevitable. The present communication has been addressed to the removal of Cr(VI) from aqueous solutions by nanoparticles of iron. Nanoparticles of iron were prepared by sol–gel method. The characterisation of the nanoparticles was carried out by XRD and TEM analysis. Batch experiments were adopted for the adsorption of Cr(VI) from its solutions. The effect of different important parameters such as contact time and initial concentration, pH, adsorbent dose, and temperature on removal of chromium was studied. The removal of chromium increased from 88. 5% to 99.05% by decreasing its initial concentration from 15 to 5 mg L−1 at optimum conditions. Removal of Cr(VI) was found to be highly pH dependent and a maximum removal (100%) was obtained at pH 2.0. The process of removal was governed by first and pseudo‐second‐order kinetic equations and their rate constants were determined. The process of removal was also governed by intraparticle diffusion. Values of the thermodynamic parameters viz. ΔG°, ΔH°, and ΔS° at different temperatures were determined. The data generated in this study can be used to design treatment plants for chromium rich industrial effluents. Adsorption results indicate that nanoiron particles can be effective for the removal of chromium from aqueous solutions.
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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) | 0.000 | 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".