Synthesis and Performance of Photocatalysts for Photocatalytic Hydrogen Production: Future Perspectives
Bibliographic record
Abstract
Photocatalysis for “green” hydrogen production is a technology of increasing importance that has been studied using both TiO2–based and heterojunction composite-based semiconductors. Different irradiation sources and reactor units can be considered for the enhancement of photocatalysis. Current approaches also consider the use of electron/hole scavengers, organic species, such as ethanol, that are “available” in agricultural waste, in communities around the world. Alternatively, organic pollutants present in wastewaters can be used as organic scavengers, reducing health and environmental concerns for plants, animals, and humans. Thus, photocatalysis may help reduce the carbon footprint of energy production by generating H2, a friendly energy carrier, and by minimizing water contamination. This review discusses the most up-to-date and important information on photocatalysis for hydrogen production, providing a critical evaluation of: (1) The synthesis and characterization of semiconductor materials; (2) The design of photocatalytic reactors; (3) The reaction engineering of photocatalysis; (4) Photocatalysis energy efficiencies; and (5) The future opportunities for photocatalysis using artificial intelligence. Overall, this review describes the state-of-the-art of TiO2–based and heterojunction composite-based semiconductors that produce H2 from aqueous systems, demonstrating the viability of photocatalysis for “green” hydrogen production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".