Hydrogen evolution via noble metals based photocatalysts: A review
Bibliographic record
Abstract
In recent decades, the use of photocatalysts in the evolution of hydrogen (H2) has received much attention. However, the use of the well-known titanium oxide and another photocatalyst as a base for noble metals is limited due to their major weakness in electron-hole pair separation. The use of cocatalysts can be a good way to overcome this problem and provide better performance for the evolution of hydrogen. In this review, suitable high-efficiency cocatalysts for solar hydrogen production have been thoroughly reviewed. New strategies and solutions were examined in terms of increasing the recombination of charge carriers, designing reactive sites, and enhancing the wavelengths of light absorption. Several new types of cocatalysts based on semiconductors in noble groups and dual metals have been evaluated. It is expected that these photocatalysts will be able to reduce the activation energy of reaction and charge separation. In this regard, the existing views and challenges in the field of photocatalysts are presented. The characteristics of monoatomic photocatalysts are reviewed in this manuscript and the latest advances in this field are summarized. Further, the future trends and upcoming research are also briefly discussed. Finally, this review presents noble metal-based photocatalysts for providing suitable photocatalysts on a larger scale and improving their applicability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".