Effect of Background Water Matrices on Pharmaceutical and Personal Care Product Removal by UV-LED/TiO2
Bibliographic record
Abstract
In this study, we evaluated the effectiveness of UV-LED-irradiated TiO2 in removing 24 commonly detected PPCPs in two water matrices (municipal wastewater effluent and Suwannee River NOM–synthetic water) and compared their performance with that of ultrapure water. Relatively fast removal kinetics were observed for 29% and 12% of the PPCPs in ultrapure water and synthetic surface water, respectively (kapp of 1–2 min−1). However, they all remained recalcitrant to photocatalysis when using wastewater effluent as the background matrix (kapp < 0.1 min−1). We also observed that the pH-corrected octanol/water partition coefficient (log Dow) correlated well with PPCP degradation rate constants in ultrapure water, whereas molecular weight was strongly associated with the rate constants in both synthetic surface water and wastewater. The electrical energy per order (EEO) values calculated at the end of the experiments suggest that UV-LED/P25 can be an energy-efficient method for water treatment applications (2.96, 4.77, and 16.36 kW h m−3 in ultrapure water, synthetic surface water, and wastewater effluents, respectively). Although TiO2 photocatalysis is a promising approach in removing PPCPs, our results indicate that additional challenges need to be overcome for PPCPs in more complex water matrices, including an assessment of photocatalytic removal under different background water matrices.
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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 | 0.000 |
| 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 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".