Disinfection of Outdoor Livestock Water Troughs: Effect of TiO2-Based Coatings and UV-A LED
Bibliographic record
Abstract
The control of pathogens is of great importance to maintaining safe water quality for animal consumption and reducing the spread of pathogens in the environment and throughout the production chain. Titanium dioxide (TiO2) is an attractive nanoparticle for disinfection purposes because it is easy to use, highly effective under UV radiation and cost effective. The goal of this study was to assess the disinfection effectiveness of TiO2-coated materials (high-density polyethylene, HDPE and stainless steel, SS) and UV-A LED light of non-coated materials, and the impacts of temperature and bacteria concentration in disinfection. Three TiO2 composites, two synthesized and one commercial (namely, TiO2, Ag- TiO2 and P25 TiO2), were assessed for their removal photocatalytic efficiency of methylene blue (10 mg/L). P25 TiO2 showed fast photocatalytic efficiency after two hours of treatment, reaching 98% efficiency after 4 h. The immobilization method M1 (fast cured epoxy) of particles in the material showed the best adhesion to substrates (scale = 4 ASTM D 3359) and for TiO2-coated stainless steel in a pre-disinfection test at a temperature of 32.3 °C and efficiency of 55.2%. There were statistically significant differences in disinfection treatments between of TiO2-coated and non-coated materials under the influence of UV-A LED light (p < 0.05) at a control temperature of 26 °C. The resulting disinfection efficiencies for typical trough materials (coated (C) or non-coated (NC) HDPE; and SS) were ranked as follows: SS-C-Light (100%) > SS-Light (81.4%) > HDPE-C-Light (63.9%) > HDPE-Light (51.3%). High ambient temperature and initial bacteria concentration tended to reduce the disinfection efficiency. The presence of TiO2 on the coated surface was confirmed using a scanning electron microscope (SEM) and energy dispersive X-ray microanalyses (EDS). These results demonstrate the disinfection potential of TiO2-coated materials and UV-A LED light, and thus, they should be considered as valuable alternatives to deal with persistent E. coli contamination of cattle troughs.
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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".