Removal of arsenic (<scp>III</scp>) and arsenic (V) from aqueous solutions through adsorption by Fe/Cu nanoparticles
Bibliographic record
Abstract
Abstract BACKGROUND While various iron‐based nanomaterials have been studied for the removal of arsenic from groundwater or its immobilization in soils, this study focuses on the applicability of iron/copper bimetallic nanoparticles for removal of arsenic from synthetic contaminated waters. In order to determine the effectiveness of these nanoparticles for arsenic removal, after synthesis, various sorption tests were performed with aqueous arsenic solutions. RESULTS Detailed physicochemical characterization of synthesized nanoparticles confirmed the successful formation of Fe/Cu nanoparticles with a mean diameter of 13.17 nm. These nanoparticles were found to be effective for removing arsenic from aqueous solutions. The maximum sorption capacities for As(III) and As(V) were 19.68 mg g−1 and 21.32 mg g−1, respectively, at a pH of 7.0. Adsorption isotherms fit well into the Langmuir equation, and sorption follows pseudo‐second‐order kinetics. Coexisting carbonate, sulfate, and phosphate ions had no significant effect on the removal efficiency of arsenic at the concentrations studied. Arsenic removal efficiency by Fe/Cu nanoparticles is enhanced in acidic environments and in basic conditions, desorption of arsenic is possible. CONCLUSION The Fe/Cu nanoparticle powder was found to be effective for removal of arsenic from water and has potential to be used for arsenic remediation from the aquatic environment or in situ immobilization of arsenic. © 2017 Society of Chemical Industry
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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".