Novel flower-like SnIn4S8/WO3 S-scheme heterojunction photocatalysts for enhanced oxidation of 5-hydroxymethylfurfural
Bibliographic record
Abstract
We developed a chemically bonded SnIn 4 S 8 /WO 3 S-scheme heterostructure photocatalyst for solar-driven selective oxidation of HMF to DFF. The S-scheme charge transfer mechanism was confirmed by DFT calculations and experimental results, offering new insights for advancing photocatalytic biomass conversion. Solar biomass conversion has garnered significant research attention, but the rapid recombination of electrons and holes in photocatalysts hinders efficiency. To enhance this process, researchers aim to develop S-scheme heterojunction photocatalysts with optimized band structures that enable effective electron-hole separation, thereby improving overall efficiency. Herein, chemical-bonded SnIn 4 S 8 /WO 3 S-scheme heterostructure photocatalyst was constructed via in-situ hydrothermal strategy for sunlight-driven catalytic selective oxidization of 5-hydroxymethylfurfural (HMF) into valuable 2,5-dimethylfuran (DFF). X-ray photoelectron spectroscopy (XPS) results prove the formation of a W–S chemical bond in the composites, which will likely enhance the efficient transport of photogenerated charges. The optimal SnIn 4 S 8 /WO 3 exhibited an excellent HMF conversion rate (89%) and DFF yield (68%) after 2 h. The S-scheme charge transfer pathway in the SnIn 4 S 8 /WO 3 composite structure was verified through density functional theory (DFT) calculations and supported by partial in situ experimental results. This study demonstrates that the S-scheme heterostructure based on SnIn 4 S 8 offers innovative insights for advancing photocatalytic biomass conversion.
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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".