Photoelectrochemical Degradation of 4-Nitrothiophenol and In Situ Surface-Enhanced Raman Spectroscopy Monitoring Based on Au Nanoparticles Grown on Templated TiO<sub>2</sub>
Bibliographic record
Abstract
Coupled photoelectrochemistry and surface-enhanced Raman spectroscopy (SERS) provide a unique platform for monitoring photocatalyzed and photoelectrocatalyzed reactions. The combination of catalysis and detection platforms typically requires sophisticated synthesis procedures, which make them prohibitive to implement. In this work, we have designed and fabricated a controllable array of Au nanoparticles (NPs) on a templated TiO 2 substrate for the simultaneous photoelectrochemical degradation and in situ electrochemical-SERS monitoring, where 4-nitrothiophenol (4NTP) was used as a molecular probe. The effect of the dimension of the Au NPs was studied, showing that the array of 42.2 nm diameter Au NPs exhibited the best degradation kinetics of 4NTP at 0.017 min –1 and the highest SERS response for detection and monitoring. Polarization modulation infrared reflection absorption spectroscopy and density functional theory calculations were further employed to understand the 4NTP response to the applied electric field. It was found that the angle of adsorbed 4NTP was highly dependent on the applied potential, resulting in changes in the effective dipole moment of the molecule. The implementation of this combined platform provides insights into surface reaction kinetics for photocatalyzed reactions on metal oxide surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".