Optimizing the photochemical conversion of UV–vis light of silver-nanoparticles decorated TiO <sub>2</sub> nanotubes based photoanodes
Bibliographic record
Abstract
Abstract Homogeneous decoration of TiO 2 nanotubes (NTs) by Ag metallic nanoparticles (NPs) was carried out by a relatively simple photoreduction process. This Ag-NPs decoration was found to improve the photoconversion efficiency of the TiO 2 -NTs based photoanodes. The x-ray photoelectron spectroscopy and x-ray diffraction analyses confirmed that all the Ag-NPs are metallic and the underlying TiO 2 -NTs crystallize in the anatase phase after their annealing at 400 °C, respectively. Transmission electron microscopy observations have confirmed the effective decoration of the TiO 2 -NTs’ surface by Ag-NPs, and allowed to measure the average Ag-NPs size, which was found to increase linearly from (4 ± 2) nm to (16 ± 4) nm when the photoreduction time is increased from 5 to 20 min. The diffuse reflectivity of the Ag-NPs decorated TiO 2 -NTs was found to decrease significantly as compared to the undecorated TiO 2 -NTs. Interestingly, the Ag-NPs decorated TiO 2 -NTs exhibited a significantly enhanced photochemical response, under visible radiation, with regards to the undecorated NTs. This enhancement was found to reach its maximum for the TiO 2 -NTs decorated with Ag-NPs having the optimal average diameter of ∼8.5 nm. The maximum photoconversion efficiency of Ag-NPs decorated TiO 2 -NTs was about two times greater than for the undecorated ones. This improved photo-electro-chemical response is believed to be associated with the additional absorption of visible light of Ag-NPs through the localized surface plasmon resonance phenomenon. This interpretation is supported by the fact that the photoluminescence intensity of the Ag-NPs decorated TiO 2 -NTs was found to decrease significantly as compared to undecorated NTs, due to charge carriers trapping in the Ag NPs. This demonstrates that Ag-NPs decoration promotes photogenerated charges separation in the TiO 2 -NTs, increasing thereby their capacity for current photogeneration. The surface decoration of TiO 2 NTs by noble metals NPs is expected to impact positively the use of TiO 2 -NTs based photoanodes in some energetic applications such as hydrogen generation and photo-electrochemical solar cells.
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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 | 0.001 | 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".