Comparative Study of Four TiO2-Based Photocatalysts to Degrade 2,4-D in a Semi-Passive System
Bibliographic record
Abstract
In this study, the relative efficiency of four forms of supported titanium dioxide (TiO2) as a photocatalyst to degrade 2,4-dichlorophenoxyacetic acid (2,4-D) in Killex®, a commercially available herbicide was studied. Coated glass spheres, anodized plate, anodized mesh, and electro-photocatalysis using the anodized mesh were evaluated under an ultraviolet – light-emitting diode (UV-LED) light source at λ = 365 nm in a semi-passive mode. Energy consumption of the system was used to compare the efficiency of the photocatalysts. The results showed both photospheres and mesh consumed approximately 80 J/cm3 energy followed by electro-photocatalysis (112.2 J/cm3), and the anodized plate (114.5 J/cm3). Although electro-photocatalysis showed the fastest degradation rate (K = 5.04 mg L−1 h−1), its energy consumption was at the same level as the anodized plate with a lower degradation rate constant of 3.07 mg L−1 h−1. The results demonstrated that three-dimensional nanotubes of TiO2 surrounding the mesh provide superior degradation compared to one-dimensional arrays on the planar surface of the anodized plate. With limited broad-scale comparative studies between varieties of different TiO2 supports, this study provides a comparative analysis of relative degradation efficiencies between the four photocatalytic configurations.
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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 | 0.001 | 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".