Design and Modification of TiO<sub>2</sub> Nanostructured Photocatalysts Using Scanning Electrochemical Microscopy-Based Techniques Coupled with Optical Spectroscopy
Bibliographic record
Abstract
In situ techniques for the screening of new nanomaterials and nanocomposites can improve and expedite the development of photocatalysts and electrocatalysts. By combining scanning electrochemical microscopy-based techniques with optical spectroscopy, we modified TiO 2 thin films with Au nanoparticles and investigated insights associated with their photoenhancement. The TiO 2 coating was optimized utilizing this high-throughput strategy in terms of film thickness, crystallinity, and structure. It was found that 50 nm of sputtered Ti on FTO glass annealed at 550 °C for 5 h resulted in optimal optical properties and photocurrent responses. The photodeposition of Au nanoparticles on the TiO 2 surface was investigated, which revealed that an optimized photocurrent response was achieved with 60 s of illumination. The coupled nanoparticle synthesis and in situ analysis enabled the rapid optimization of the deposition and monitoring through UV–vis spectroscopic, electrochemical, and photoelectrochemical techniques. This advanced approach will facilitate the high-throughput design of efficient photoelectrochemical catalysts for sustainable energy and clean environmental applications.
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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) | 0.000 | 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".