Disinfection and Photocatalytic Degradation of Organic Contaminants Using Visible Light-Activated GCN/Ag2CrO4 Nanocomposites
Bibliographic record
Abstract
Visible-light-driven photocatalysts have gained increasing attention in the past few decades in treating emerging contaminants in water and wastewater. In this work, the photocatalytic activity of the coupled graphitic carbon nitride (GCN) and silver chromate (Ag2CrO4), herein denoted as GCN/Ag2CrO4, nanocomposites was evaluated for degrading organic pollutants and inactivating microorganisms under visible light irradiation using a royal blue light-emitting diode (LED). The organic pollutants studied were 2,4-dichlorophenoxyacetic acid (2,4-D) and methyl chlorophenoxy propionic acid (MCPP or Mecoprop-P) present in KillexR, a commercially available herbicide, bovine serum albumin (BSA) protein, and SARS-CoV-2 spike protein. The disinfection experiments were conducted on wastewater secondary effluent. The results showed that over 85% degradation was achieved for both 2,4-D and Mecoprop-P in 120 min while 100% of BSA protein and 77.5% of SARS-CoV-2 protein were degraded in 20 min and 30 min, respectively. Additionally, GCN/Ag2CrO4 nanocomposites led to over one log reduction of cellular ATP (cATP), total coliforms, and E. coli in wastewater treatment plant (WWTP) secondary effluent after 60 min of royal blue LED irradiation. It was observed that the degradation performance of a photocatalyst under light irradiation is contaminant-specific. The binding affinity of the released metal ions from GCN/Ag2CrO4 with protein and ATP functional groups was responsible for the degradation of proteins and the reduction of cATP, while the generated ROS was responsible for the disinfection of total coliforms and E. coli. Overall, the results indicate that GCN/Ag2CrO4 nanocomposite is a promising photocatalyst in degrading organic pollutants and disinfecting microorganisms under visible light irradiation within a reasonable time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".