Electrocatalytic Enhancement of Salicylic Acid Oxidation at Electrochemically Reduced TiO<sub>2</sub> Nanotubes
Bibliographic record
Abstract
In this study, TiO 2 nanotubes were treated via electrochemical reduction and tested as a novel catalyst, for the first time, toward the electrochemical oxidation of salicylic acid (SA), where the effects of cathodic current and reduction time were systemically investigated. The fabricated TiO 2 nanotubes were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Cyclic voltammetry (CV), chronoamperometry, chronopotentiometry, ultraviolet–visible light (UV-vis) absorbance spectroscopy, and Mott–Schottky plots were employed to study the enhanced electrochemical activity of the TiO 2 nanotubes. Our experimental results revealed that the optimal electrochemical treatment conditions were −5 mA cm –2 for 10 min. The treated TiO 2 nanotubes possessed a much higher overpotential for oxygen evolution than a Pt electrode, and exhibited a high electrocatalytic activity toward the oxidation of SA. The oxidation of SA at the treated TiO 2 nanotubes was shown to be 6.3 times greater than a Pt electrode. Stability tests indicated that treated TiO 2 nanotubes are very stable over eight cycles of electrochemical oxidation of SA. The high electrocatalytic activity and stability of the treated TiO 2 nanotubes enabled by the facile electrochemical reduction can be attributed to the decrease of Ti(IV), the increase of Ti(II) and Ti(III), and the increase of the oxygen vacancies, as well as significant improvement of the donor density.
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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".