Photoelectrochemical Oxidation of Organic Pollutants By TiO<sub>2</sub> Based Nanostructured Catalysts
Bibliographic record
Abstract
Advanced oxidative processes (AOPs) have been explored extensively for their application in waste water treatment and remediation. Their use of reactive oxygen species (ROS) to degrade organic pollutants that make their way into waterways shows promise for the complete removal of toxic materials. The implementation of electrocatalysts and photocatalysts as platforms the production of ROS allows for a green approach to AOPs, as it requires less additives for wastewater treatment and water purification. The use of catalysts for this process can often be economically unviable, as they commonly employ expensive noble-metals. To remedy this, the morphological and electrochemical character of more abundant materials, including metal-oxides such as TiO2, have been modified to increase their photocatalytic and electrocatalytic properties. In this presentation, we report on the development of nanostructured TiO2 catalysts for the photoelectrochemical degradation of atrazine, a common herbicide used in North America. Nanoporous TiO2 was directly grown on a titanium substrate using an anodization process. Characterization of the TiO2 electrodes was carried out by scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction. Electrochemical treatment of the nanoporous TiO2 was performed to further increase its catalytic activity towards the photoelectrochemical degradation of atrazine. The degradation process was monitored by UV-Vis spectroscopy, total organic carbon analysis and liquid chromatography-mass spectrometry. The effectiveness in the removal of Atrazine and the degradation mechanism will be discussed.
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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".