Pharmaceutical Micropollutant Treatment with UV–LED/TiO2 Photocatalysis under Various Lighting and Matrix Conditions
Bibliographic record
Abstract
The persistence of pharmaceuticals and personal care products (PPCPs) in water has been a cause for concern for several years. Many studies have successfully used TiO2/UV photocatalysis to remove these compounds from water. In order to optimize these systems for large-scale water treatment, the effects of the reaction matrix, methods to improve energy efficiency, and methods for easy catalyst separation must be considered. The following study examines the photocatalytic degradation of a cocktail of 18 PPCPs using a porous titanium–titanium dioxide membrane and the effect of solution pH on kinetic rate constants. The addition of methanol to the reaction—commonly used as a carrier solvent—had a significant effect on kinetic rate constants even at low concentrations. Solution pH was also found to influence kinetic rate constants. Compounds had higher kinetic rate constants when they were oppositely charged to the membrane at experimental pH as opposed to similarly charged, suggesting that electrostatic forces have a significant effect. The controlled periodic illumination of UV–LEDs was also investigated to increase photonic efficiency. The dual-frequency light cycle used did not cause a decrease in degradation for many compounds, successfully increasing the photonic efficiency without sacrificing performance.
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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 | 0.001 |
| 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".