Removal of chloride ion from drinking water using Ag NPs-Modified bentonite: Characterization and optimization of effective parameters by response surface methodology-central composite design
Bibliographic record
Abstract
The presence of chloride ion as an environmental pollutant is having a devastating and irreversible effect on aquatic and terrestrial ecosystems. To ensure safe and clean drinking water, it is vital to remove this substance using non-toxic and eco-friendly methods. This study presents a novel and highly efficient Ag NPs-modified bentonite adsorbent for removing chloride ion, a common environmental pollutant, from drinking water using a facile approach. The surface chemical properties and morphology of the pristine Na-bentonite and Ag NPs-Modified bentonite were characterized by field emission scanning electron microscopy (FESEM) and X-ray spectroscopy (EDX), X-Ray diffraction (XRD), Fourier transform infrared (FTIR), and zeta potential (ζ). To achieve maximum chloride ion removal, the effects of experimental parameters, including adsorbent dosage (1-9 g/L), chloride ion concentration (100-900 mg/L), and reaction time (5-25 h), were examined using the Response Surface Methodology (RSM). The chloride ion removal of 90% was obtained at optimum conditions (adsorbent dosage: 7 g/L, chloride ion concentration: 500 mg/L, and reaction time: 20 h). The adsorption isotherm and kinetics results indicated that the Langmuir isotherm model and pseudo-second-order kinetics were found suitable to chloride ion removal. Additionally, the regeneration and reusability of the Ag NPs-modified bentonite were further studied. In the regeneration and reusability study, the Ag NPs-modified bentonite has shown consistently ≥90% and ≥87% chloride ion removal even up to 2 repeated cycles, separately. Thus, the findings in this study provided convincing evidence for using Ag-NPs modified bentonite as a high-efficiency and promising adsorbent to remove chloride ion from drinking water.
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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 | 0.002 | 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 | 0.001 |
| 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".