Fabrication of g-C<sub>3</sub>N<sub>4</sub>/NiO/ZnO based Ternary Nanocomposite for Efficient Photocatalytic Degradation of Methylene Blue
Bibliographic record
Abstract
Photocatalysis has emerged as a widely recognized and environmentally friendly technique for the degradation of biological contaminants into less hazardous substances. The technique offers a sustainable path for contamination reduction by utilizing sunlight-activated catalysts to initiate reactions for the degradation of pollutants. In this work, a facile co-precipitation approach was utilized to synthesize a ternary nanocomposite-based photocatalyst to improve photocatalytic performance. The metal oxide semiconductors ZnO and NiO were successfully integrated into the g-C3N4 matrix to develop a photocatalyst that showed substantially increased photocatalytic activity. The synthesized ternary nanocomposite was investigated for different physicochemical techniques such as photoluminescence (PL), ultraviolet-visible (UV-Vis) absorption spectroscopy, scanning electron microscopy (SEM), Raman spectroscopy, and X-ray diffraction (XRD). The structural, morphological, and optical characteristics of the ternary nanocomposite were thoroughly explored by these physiochemical techniques. The synthesized CNZ ternary nanocomposites-based photocatalyst revealed a significantly enhanced photocatalytic degradation rate of 92%, outperforming all other samples. The ternary nanocomposite demonstrated excellent reusability even after five successive reaction cycles, unveiling the superior potential in the photocatalytic application for an extended period without significantly losing its effectiveness. The synergistic integration of NiO and ZnO into g-C3N4 boosted the photocatalytic activity by enhancing electron-hole separation and reducing recombination reactions. The hybrid photocatalyst offers a great deal of promise for effectively eliminating harmful pollutants 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) | 0.001 | 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".