Dual-function Cameroonian clay-supported ZnO and TiO₂ photocatalysts for ibuprofen mineralization and bacterial inactivation
Bibliographic record
Abstract
The increasing presence of pharmaceuticals, such as ibuprofen, in wastewater poses significant environmental and public health challenges, particularly in developing regions. In this study, we developed photocatalytic materials by doping natural Cameroonian clay with ZnO and TiO₂ to achieve efficient ibuprofen mineralization and bacterial inactivation under ultra-violet (UV) light. Characterization confirmed the successful integration of semiconductors into the clay matrix, which enhanced the surface area to 325 m²/g for TiO₂-based composites. Under UVA irradiation (1.2 mW/cm²), the Cu-doped TiO₂/clay composite achieved 48 % ibuprofen mineralization, measured by Total Organic Carbon (TOC) reduction, within 4 h, while ZnO-based composites reached up to 23 % under similar conditions. Antibacterial tests demonstrated complete inhibition of Shigella spp., total coliforms, and faecal streptococci at a catalyst dosage of 1 g/L under UVA, highlighting the dual functionality of the materials. These low-cost, locally sourced photocatalysts show promise for integrated pharmaceutical and microbial removal in decentralized wastewater treatment systems, offering a sustainable solution for water purification in resource-limited settings. • Synthesized and characterized clay-based photocatalysts doped with ZnO and TiO₂ using natural Cameroonian smectite. • Achieved up to 48 % ibuprofen mineralization under UVA in 4 h, confirmed by Total Organic Carbon (TOC) analysis. • Demonstrated antibacterial activity against Shigella spp., total coliforms, and faecal streptococci at 1 g/L under UVA. • Developed a low-cost, eco-friendly material from local resource for sustainable wastewater treatment in developing regions. • Dual functionality for simultaneous pharmaceutical degradation and microbial inactivation in decentralized systems.
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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".