Simultaneous removal of multiple organic micropollutants via UV-visible light driven BiVO4/TiO2-GO photoanode: Experimental and CFD study
Bibliographic record
Abstract
• BiVO 4 /TiO 2 -GO photoanode achieves 50 % higher OMP removal vs pristine BiVO 4 . • CFD model predicts 80 % simultaneous removal of BTA, CBZ, CAF and DIC within 25 mins. • A light intensity of 400 W m −2 optimizes reactor performance for OMP removal. • Turbulent flow enhances OMP diffusion, accelerating removal kinetics. • Ultrasonic spray pyrolysis enables successful fabrication of heterojunction photoanodes. In this study, we investigated the use of BiVO 4 /TiO 2 -GO heterojunction photoanode in a PEC based AOP to simultaneously remove four organic micropollutants (OMPs): benzotriazole (BTA), carbamazepine (CBZ), caffeine (CAF) and diclofenac (DIC) from demineralized water. Each OMP had an initial concentration of 40 µg L −1 . Ultrasonic spray pyrolysis (USP) was used to deposit BiVO 4 and TiO 2 -GO layers on fluorine doped tin oxide (FTO) electrodes. The heterojunction photoanode at an applied voltage of 1 V (vs Ag/AgCl) achieved simultaneous removal efficiencies of 100 % for DIC, 54 % for CBZ, 36 % for BTA and 33 % for BTA under simulated solar light. Compared to the pristine BiVO 4 photoanode, the heterojunction photoanode showed 50 % higher removal efficiency for BTA, CBZ and CAF. The reaction kinetics revealed that the first order rate coefficient for DIC removal was about nine times higher than that of CBZ and fifteen times higher than those of BTA and CAF. To assess scalability, a computational fluid dynamics (CFD) model incorporating the experimentally determined reaction kinetics was developed for a conceptually designed up-scaled PEC reactor. The model analyzed the effect of reactor design and fluid flow conditions on the removal of OMPs. Under turbulent flow conditions, enhanced removal efficiency was observed for all four OMPs, which was attributed to the effects of eddy diffusion and convective mixing. The optimized reactor design under turbulent flow condition achieved an 80 % removal efficiency for all four OMPs within 25 min under a light intensity of 400 W m −2 . The findings highlight the potential of BiVO 4 /TiO 2 -GO heterojunction photoanodes for efficient and scalable PEC water treatment, showing a promising approach for the elimination of OMPs from wastewater.
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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 | 0.001 | 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".