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
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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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".