Photocatalytic Versus Stoichiometric Hydrogen Generation Using Mesoporous Silicon Catalysts: The Complex Role of Sacrificial Reagents
Bibliographic record
Abstract
Abstract Mesoporous silicon has emerged as a promising photocatalyst for solar‐driven hydrogen production via water splitting. However, its cyclability and stability are poor because silicon oxidizes during the reaction, which also produces stoichiometric amounts of hydrogen. Despite this, the exact contribution of the stoichiometric component in the reaction remains unknown. This study demonstrates that the stoichiometric hydrogen contribution when using silicon photocatalysts is dependent on the type of sacrificial reagent. In the presence of triethanolamine and sodium sulfite, which increase the solution's pH, over 90% of the hydrogen produced originates from the stoichiometric reaction. In contrast, when alcohol‐based sacrificial reagents are used, the ratio of catalytic to stoichiometric hydrogen depends on the size of the alcohol molecule. Smaller alcohols, such as methanol and ethanol, result in higher overall hydrogen production; however, more than 40% of it originates from the stoichiometric reaction of silicon with water. As the alcohol size increases, the amount of water near the catalyst surface is limited, leading to decreased hydrogen production rates but improved photocatalyst stability. This study highlights the major role of the undesirable side reactions in silicon based photocatalysis and the need for more rigorous hydrogen quantification protocols to determine true photocatalytic activity.
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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".