Ag/AgCl nanoparticles: photocatalytic degradation of bentazon and adsorption of chromium oxide for sustainable water treatment under optimal pH conditions
Bibliographic record
Abstract
This study introduces a novel approach using Ag/AgCl nanoparticles (NPs) synthesized via a pH-controlled co-precipitation method for the dual removal of organic and inorganic pollutants. Unlike conventional photocatalysts, the synthesized Ag/AgCl NPs demonstrated high degradation efficiency for bentazon and significant adsorption capacity for Cr2O3 under natural sunlight. The pH-tuned synthesis not only enhanced crystallite structure and catalytic activity but also enabled efficient performance in visible-light-driven processes, making the system eco-friendly, cost-effective, and suitable for real wastewater treatment applications. Ag/AgCl NPs were synthesized via the co-precipitation method and characterized using UV–Vis spectroscopy, FTIR, XRD, SEM, EDX, and zeta potential measurements to evaluate their structural, morphological, and optical properties. The crystallite size and phase composition of Ag/AgCl NPs were found to be pH-dependent, with the smallest crystallite size (13.29 ± 2.48 nm) observed at pH 8, which also corresponded to the highest photocatalytic efficiency. Photodegradation studies demonstrated that Ag/AgCl NPs achieved a maximum bentazon degradation efficiency of 92.47 ± 5.55% within 180 minutes under slightly alkaline conditions (pH 8) and 87.2 ± 5.23% for Cr2O3 degradation. These findings highlight the potential of Ag/AgCl NPs for environmental remediation, particularly in wastewater treatment applications, where pH optimization plays a critical role in enhancing catalytic performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".