Enhancing Photon Transfer Efficiency in Photocatalysis Using Suspended LED Lights for Water Treatment
Bibliographic record
Abstract
Photocatalysis application in water treatment has been the object of many researchers worldwide in recent decades. However, there are limited commercial applications due to low photon transfer efficiency, which create barriers leading to challenges in making the process efficient and economically feasible. Fixed UV/visible light sources, which are generally located outside the reactor or encapsulated in quartz tube inside the reactor are the source of energy to activate photocatalyst generating powerful oxidants such as electrons and holes. Suspended waterproof LED visible lights were employed to enhance photon transfer efficiency. Consequently, the required energy was lower resulting in negligible temperature increase and eliminated the need for an external cooler, no need for quartz (UV transparent) or treated glass reactors, enhanced mixing due to continuous movement of light bulbs by convective currents, and minimum/no attenuation. Direct Blue 15 (DB15) dye was used as model compound and the photocatalyst was P25 TiO2 (Average particle: 30 nm, Surface area: 50 m2 g−1). The samples taken at different time intervals were analyzed by UV-Vis. spectrophotometer (UV-3600), and TOC-V CPN total organic carbon analyzer (both from Shimadzu). It was found that for the same level of degradation, the degradation rate increased by about 50% compared to conventional fixed light photoreactor. Overall, the cost of the operation can be reduced substantially, paving the road for feasible commercialization of the process.
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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".