Passive Solar Photocatalytic Treatment of Emerging Contaminants in Water: A Field Study
Bibliographic record
Abstract
Global economic shifts towards utilization of solar energy provides opportunities for photocatalytic technologies that can harness this abundant source of energy for treatment of organic contaminants. The majority of studies in this area have been performed under artificial light, whereas in this paper, the efficacy of passive photocatalysis was studied under sunlight. Buoyant titanium dioxide (TiO2) coated glass spheres were used to treat 2, 4-dichlorophenoxy acetic acid (2, 4-D), methyl chlorophenoxy propionic acid (MCPP), and 3, 6-Dichloro-2-methoxy benzoic acid (Dicamba) in Killex®, a commercially available herbicide. Furthermore, photocatalytic degradation of sulfolane and a typical naphthenic acid (cyclopentane carboxylic acid—CPA) were also tested under ambient conditions. The results showed 99.8% degradation of 2, 4-D, 100% degradation of both MCPP and Dicamba in Killex® solution, and 97.4% degradation of sulfolane by capturing 3.18 MJ/m2 solar energy. Total organic carbon (TOC) was decreased by 88% and 64% in both solutions, respectively. TOC of the aqueous solution containing 20 ppm CPA was also decreased by 78.4% with 7.8 MJ/m2 energy. Despite the slow kinetics and the temporal variations of sunlight in northern latitudes, the results indicated that passive photocatalysis is a promising approach for treatment of contaminants under ambient conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".