Green Photocatalytic Oxidation of Benzyl Alcohol over Noble-Metal-Modified H<sub>2</sub>Ti<sub>3</sub>O<sub>7</sub> Nanowires
Bibliographic record
Abstract
One-dimensional H 2 Ti 3 O 7 nanowires (NWs) supported by Au, Ag, and Pd monometallic nanoparticles (NPs) and Au–Pd bimetallic NPs were prepared and used for photochemical benzyl alcohol oxidation. Techniques, such as XRD, TEM, XPS, N 2 physisorption, and diffuse reflectance ultraviolet–visible (DRUV–vis), were used to characterize the obtained catalysts. These results showed that the Pd/H 2 Ti 3 O 7 NWs catalyst under light irradiation displayed an enhanced photocatalytic performance, nearly 2.6 times higher than that for the catalyst without irradiation and about 1.5 times higher than that for Pd/TiO 2 (P25) with irradiation. The enhanced benzyl alcohol oxidation activity for Pd/H 2 Ti 3 O 7 NWs might be due to the favorable synergetic effect between Pd and H 2 Ti 3 O 7 NWs. Particularly, highly dispersed Pd NPs with about 10.2 nm on H 2 Ti 3 O 7 NWs can promoted the light harvesting ability. The well-matched contact boundary between H 2 Ti 3 O 7 NWs and Pd NPs might promote separation for the photoinduced electron and hole pairs. After 5 recycle utilization cycles, there was no evident decline in activity and selectivity for the Pd/H 2 Ti 3 O 7 NWs catalyst, which still maintained its original structure of H 2 Ti 3 O 7 NWs. The current study provides a potential application in the green and highly efficient photocatalytic synthesis of other organic compounds and other environmental 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) | 0.000 | 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".