CuO@TiO2 and NiO@TiO2 core-shell catalysts for hydrogen production from the photocatalytic reforming of glycerol aqueous solution
Bibliographic record
Abstract
Hydrogen production from the photocatalytic reforming of glycerol aqueous solution was performed on the CuO@TiO2, NiO@TiO2, NiO@CuO, and CuO@NiO core-shell nanostructured catalysts under simulated solar light irradiation. These catalysts were prepared by the combination of a modified sol-gel and a precipitation-deposition method using hydroxypropyl cellulose as structural linker and they were characterized by powder X-ray diffraction (XRD), UV-Vis diffuse reflectance spectroscopy (UV–Vis DRS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and nitrogen physisorption isotherms techniques. The catalysts containing TiO2 as a shell and CuO as core showed much higher activity compared with those formulated with NiO@CuO, CuO@NiO, and bared CuO or NiO nanoparticles. The highest rate of hydrogen production obtained with the CuO@TiO2 catalyst was as high as 153.8 μmol·g−1h-1, which was 29.0, 24.8, 11.2 and 3.2 times greater than that obtained on CuO@NiO, NiO@CuO, TiO2 P25, and NiO@TiO2 catalyst, respectively. For the high active CuO@TiO2 catalyst, after activation of TiO2 with solar light irradiation, the conduction band electrons can be transferred to CuO core through the heterojunction in the core-shell interfaces which led to CuO gradually reduced to Cu2O, favoring the reduction of proton to release hydrogen.
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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) | 0.001 | 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".