Enhanced photocatalytic degradation of organic contaminants in water by highly tunable surface microlenses
Bibliographic record
Abstract
Photocatalysis is one of the dominant technologies used to enhance the efficiency of water decontamination with light-based treatments. However, the effectiveness of photocatalysts is usually limited by the irradiation conditions and the properties of the water matrix. In this work, we have demonstrated the capability of surface microlenses (MLs) as a clean technology for more efficient photocatalytic water decontamination. Random or ordered surface MLs were fabricated from simple polymerization of nanodroplets produced in a solvent exchange process. Both random microlenses (MLR) and microlenses array (MLA) could enhance the photocatalytic degradation efficiency of four representative pollutants, including methyl orange (MO), norfloxacin (NFX), sulfadiazine (SFD), sulfamethoxazole (SMX), spiked in ultra-pure water, synthetic natural water, or real river water. By controlling the conditions of light treatment, the photodegradation efficiency could be enhanced by up to 402%. The effectiveness of surface MLs was validated under both visible LED light and simulated solar light and for two photocatalysts zinc oxide (ZnO) and titanium dioxide (TiO2). By reducing the concentration of the photocatalysts from 100 to 5 mg/L and the intensity of irradiation intensity from 1 Sun to 0.3 Sun, our findings suggest that the enhancement factor by MLs was higher at lower catalyst concentration, or at lower light intensity. Based on optical simulations and experimental results, we demonstrated that surface MLs optimize the light distribution and promote the formation of active species, which results in the enhancement of degradation efficiency. The use of MLs may serve as a novel strategy to improve the photocatalytic degradation of micropollutants, especially in places where the available light source is weak, such as indoors or in cloudy regions.
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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".