Efficient solar-driven degradation of a tire wear pollutant using floating K-doped g-C3N4 photocatalyst in secondary municipal wastewater
Bibliographic record
Abstract
Graphitic carbon nitride (g-C 3 N 4 ) is a promising photocatalyst for solar-driven degradation of contaminants of emerging concern (CECs). However, its powdered form complicates recovery for large-scale applications in water treatment. In this study, we developed potassium-doped g-C 3 N 4 (KCN) supported on expanded perlite (EP), a non-toxic volcanic glass, to address these recovery challenges. KCN was synthesized in situ by adding varying amounts of KOH to urea and EP, followed by calcination. The EP/KCN composites were characterized and tested for the degradation of 1,3-diphenyl guanidine (DPG), a tire wear pollutant. SEM images showed that KCN coverage increased with higher urea loading on EP, with EP/KCN20 (20:1 urea to EP, 1 wt% KOH) having nearly complete surface coverage. FT-IR spectra confirmed stronger C-N and C=N stretching vibrations with increased KCN content, confirming the successful incorporation of KCN into the composite. XRD patterns displayed distinct KCN peaks at higher loadings, while PL analysis suggested slightly reduced charge recombination in the EP/KCN20 composite, indicating enhanced photocatalytic efficiency. Furthermore, EP/KCN20 achieved 96.1 % degradation of DPG under simulated solar exposure in 5 h, with over 80 % efficiency maintained across four consecutive cycles. The composite also demonstrated robust performance under more complex conditions, including natural solar light and in DPG-spiked secondary municipal wastewater, with a cost of 5.57 USD/m 3 . These findings highlight the potential of the EP/KCN composite as a scalable, cost-effective solution for the removal of CECs from wastewater, driven by renewable solar energy.
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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".