Coproduction of Hydrogen and Value-Added Chemicals Using Ni-TiO <sub>2</sub> @g-C <sub>3</sub> N <sub>4</sub> Nanocomposites from Glycerol Photoreforming
Bibliographic record
Abstract
Photocatalytic hydrogen production is an alternative strategy that can be used to attain sustainable solar energy conversion and storage. Regarding the solar-to-hydrogen process, photocatalytic hydrogen generation in the presence of a sacrificial agent has become a viable approach. However, the nonselective oxidation of sacrificial agents is responsible for the higher cost of green hydrogen production. Hence, this work presents the strategic design of a bifunctional photocatalyst that can simultaneously generate value-added chemicals and produce hydrogen from glycerol. The glycerol photoreforming process was realized on the well-fabricated Ni-TiO 2 @g-C 3 N 4 composite photocatalysts. The combination of Ni-TiO 2 and g-C 3 N 4 not only enhanced the separation efficiency of photogenerated electrons and holes but also extended the light absorption from the ultraviolet region to the visible region. These significant promotions contributed to photocatalytic hydrogen production from glycerol while valuable chemicals of glyceraldehyde, dihydroxyacetone, and glycolic acid were selectively produced at the same time. As a result, the maximum hydrogen generation and glycerol conversion were achieved with rates of approximately 32,000 μmolg –1 h –1 and 73%, respectively, on the optimized Ni-TiO 2 @g-C 3 N 4 composite. This present work provides an important example for the coproduction of value-added compounds and sustainable hydrogen by the rational design of bifunctional photocatalysts.
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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".