Synthesis and characterization of ZnS and Ag-ZnS nanoparticles for photocatalytic degradation of aqueous pollutants
Bibliographic record
Abstract
Photocatalytic degradation has drawn much interest recently as a substitute technique for eliminating environmental contaminants from the aqueous phase. In this study, pure and Ag-doped zinc sulfide (ZnS) nanoparticles were synthesized for the photocatalytic degradation of methylene blue (MB) under UVA light irradiation using a simple chemical co-precipitation method. The nanopowders' structural, optical, morphological, and chemical properties were characterized using XRD, FTIR, UV-Vis, and FESEM techniques. XRD analysis confirmed the hexagonal crystal structure of the nanoparticles, while FTIR identified stretching vibrations corresponding to O–H, C–H, C=O, C–N, and Zn–S bonds. The UV-Vis analysis revealed an optical band gap in the range of 5.2–5.4 eV. Photocatalytic performance tests under UVA light demonstrated that Ag doping significantly enhanced the photocatalytic efficiency of ZnS nanoparticles in degrading MB. Upon exposure to UVA light, the synthesized Ag-ZnS nanoparticles achieved impressive decolorization efficiency within 25 minutes, compared to 35 minutes for pure ZnS. The findings indicate that Ag-ZnS is a highly promising photocatalyst for the efficient removal of aqueous pollutants, including methylene blue dye.
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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".