Photolysis at the Speed of Light: Chemical-Free Degradation of Trace Organic Contaminants by Bespoke Photolysis Using High-Intensity Ultraviolet C Light-Emitting Diodes
Bibliographic record
Abstract
Ultraviolet C light-emitting diodes (UV C LEDs) have demonstrated effectiveness in disinfection applications and proven suitability at scale for disinfection of municipal wastewater and drinking water. Technological advances in materials design and electrical efficiency have made high-intensity light delivery by UV C LEDs a reality and now poise these traditionally disinfection systems to serve a dual purpose for targeted remediation of trace organic contaminants (TrOCs). This work investigated the effectiveness of UV C light emission tailoring on the photodegradation dynamics of select TrOCs. Degradation kinetics and quantum yields of target compounds under 275 nm irradiation were governed by molar absorbance and chemical structure, and kinetics followed estrone (E1) > tryptophan > caffeine ≈ pCBA > urea. Secondary experiments compared the efficacy of a 275 nm UV LED and a medium-pressure mercury vapor (MP UV) system for photodegradation of two steroid estrogens, E1 and 17β-estradiol (17β-E2). Use of the 275 nm UV LED system substantially reduced fluence requirements and, in the case of 17β-E2, energy requirements, to achieve 90% degradation of the target compounds. Liquid chromatography-tandem mass spectrometry analysis of an E1 photodegradation product showed that the UV C LED system was more effective in eliminating both E1 and its associated photoproduct as compared to the MP UV system. This work demonstrates the effective use of UV LEDs for tailored photolysis of TrOCs and provides evidence for their use potential in applications outside of water disinfection.
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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) | 0.001 | 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".