Kinetics of Photoelectrocatalytic Degradation of Nitrophenols on Nanostructured TiO<sub>2</sub> Electrodes
Bibliographic record
Abstract
In the present work, titanium oxide (TiO 2 ) nanotubes were directly grown by the electrochemical oxidation of titanium substrates at 20 V in a nonaqueous electrolyte (DMSO/HF). The morphology and microstructure of the synthesized TiO 2 photocatalysts were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The kinetically photoelectrochemical degradation of 2-nitrophenol (2-NPh) and 4-nitrophenol (4-NPh) at the TiO 2 nanotubes was examined, individually and when they were mixed together. It was found that the photoelectrochemical degradation of 2-NPh is faster than that of 4-NPh. In order to determine the kinetic behavior of 2-NPh and 4-NPh in the course of the photoelectrocatalytic oxidation of their mixtures, an experimental design with 36 samples and a test set with 6 samples were used to build up a partial least-squares (PLS) model. The degradation of both 4-NPh and 2-NPh became slower in the binary mixture compared with the individual degradation rates of the 4-NPh and 2-NPh. The present work has demonstrated that UV−vis spectroscopy coupled with PLS calibration can be used to in situ monitor the concentration changes, providing a novel approach to determine the competitive effects of different organic pollutants during water purification and wastewater treatment.
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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".