Photochemical Synthesis of Radiate Titanium Oxide Microrods Arrays Supporting Platinum Nanoparticles for Photoassisted Electrooxidation of Methanol
Bibliographic record
Abstract
Abstract Photoassisted catalysis is recently adopted to accelerate the kinetics of the methanol oxidation, which allows the photocatalysis and electrocatalysis simultaneously occur on the catalyst surface and even on interior region. The rational design of highly efficient photoassisted electrocatalysts is highly desirable, however, it is very challenging. In this study, architectures of radiate TiO2 microrods arrays support Pt nanoparticles (Pt NPs/TiO2 MRs) are developed, via the combination of first hydrothermal and subsequent photodeposition process. Benefited from the synergetic effect of photocatalytic acceleration and the radiate architectures, the mass activity of Pt NPs/TiO2 MRs for methanol electrooxidation, under UV irradiation (wavelength: 365 nm), is 2.77 and 6.1 times as high as those of Pt NPs/TiO2 MRs without irradiation and commercial Pt/C, respectively. Moreover, under UV irradiation, both the CO tolerance and durability of the Pt NPs/TiO2 MRs catalysts are significantly improved. Notably, in both acidic and alkaline media, the Pt NPs/TiO2 MRs catalysts show improved electrocatalytic performance for photoassisted electrooxidation of methanol. This study provides a building art toward 3D architectures of radiate semiconductor MRs arrays supporting metallic NPs, and offers an effective way to improve the electrochemical activity of methanol oxidation utilizing the synergistic combination of photocatalysis and electrocatalysis.
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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".