Synthesis of scaly Sn<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub>nanobelt heterostructures for enhanced UV-visible light photocatalytic activity
Bibliographic record
Abstract
A novel scaly Sn3O4/TiO2 nanobelt heterostructured photocatalyst was fabricated via a facile hydrothermal route. The scaly Sn3O4 nanoflakes can be synthesized in situ and assembled on surface coarsened TiO2 nanobelts through a hydrothermal process. The morphology and distribution of Sn3O4 nanoflakes can be well-controlled by simply tuning the Sn/Ti molar ratio of the reactants. Compared with single phase nanostructures of Sn3O4 and TiO2, the scaly hybrid nanobelts exhibited markedly enhanced photoelectrochemical (PEC) response, which caused higher photocatalytic hydrogen evolution even without the assistance of Pt as a co-catalyst, and enhanced the degradation ability of organic pollutants under both UV and visible light irradiation. In addition to the increased exposure of active facets and broad light absorption, the outstanding performance is ascribed to the matching energy band structure between Sn3O4 and TiO2 at the two sides of the heterostructure, which efficiently reduces the recombination of photo-excited electron-hole pairs and prolongs the lifetime of charge carriers. Both photocatalytic assessment and PEC tests revealed that Sn3O4/TiO2 heterostructures with a molar ratio of Sn/Ti of 2/1 exhibited the highest photocatalytic activity. This study provides a facile and low-cost method for the large scale production of Sn3O4 based materials in various 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) | 0.001 | 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".