Facile and Controllable Modification of 3D In<sub>2</sub>O<sub>3</sub> Microflowers with In<sub>2</sub>S<sub>3</sub> Nanoflakes for Efficient Photocatalytic Degradation of Gaseous <i>ortho</i>-Dichlorobenzene
Why this work is in the frame
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Bibliographic record
Abstract
Novel 3D In 2 S 3 /In 2 O 3 heterostructures comprised of 3D In 2 O 3 microflowers and In 2 S 3 nanoflakes were synthesized via a facile hydrothermal process followed by an in situ anion exchange reaction. In the In 2 S 3 /In 2 O 3 heterostructures, the In 2 S 3 nanoflakes were in situ generated and uniformly assembled on In 2 O 3 microflowers. The microstructures, optical properties, oxygen vacancy concentration, and photoreactivity of the heterostructures could be tuned by adjusting the amount of sulfide source. The effect of In 2 S 3 -nanoflakes modification on the oxygen vacancy concentration, optical properties, charge carrier separation, and charge carrier lifetime of In 2 O 3 were investigated systematically. The catalytic activity of the proposed heterostructures for degradation of gaseous ortho -dichlorobenzene ( o -DCB, a representative chlorinated volatile organic compounds) was higher than that of either unmodified In 2 O 3 or TiO 2 (P25). Meanwhile, oxygen vacancies, systematically explored by Raman, X-ray photoelectron spectroscopy (XPS), and low-temperature electron spin resonance (ESR) spectroscopy, were demonstrated to have a two-side effect on the photocatalytic performance. Particularly, the main reaction products including o -benzoquinone type species, phenolate species, formates, acetates, and maleates were verified with in situ FTIR spectroscopy. Additionally, ESR examination confirmed that • OH and • O 2 – were the predominant reactive oxygen species involved in the degradation of gaseous o -DCB. The current research provides new insight into utilizing In-based heterostructures as promising and efficient visible-spectrum-responsive catalysts for the removal of harmful chlorinated volatile organic compounds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it