Photocatalytic hydrogen generation using mesoporous silicon nanoparticles: influence of magnesiothermic reduction conditions and nanoparticle aging on the catalytic activity
Bibliographic record
Abstract
In recent years, mesoporous silicon (mp-Si) nanoparticles (NPs) have been recognized as promising materials for sustainable photocatalytic hydrogen (H2) generation, which is both an important chemical feedstock and potential clean energy vector. These materials are commonly prepared via magnesiothermic reduction of silica precursors due to the ease, scalability, and tunability of this reaction. In this work, we investigate how the conditions of magnesiothermic reduction (i.e. reaction temperature and time) influence the performance of mp-Si for photocatalytic H2 generation. The mp-Si NPs were prepared using either the conventional single temperature heating method (650 °C for 3 or 6 h) or a two-temperature method in which the reaction is initially heated to 650 °C for 0.5 h, followed by a second step heating at 100 (mp-Si100), 200 (mp-Si200), or 300 °C (mp-Si300) for 6 h. Of these, mp-Si300 was the best performing photocatalyst and showed the highest H2 evolution rate (4437 μmol h-1 g-1 Si). Our results suggest that crystallinity has a profound effect on the performance of mp-Si photocatalysts. Additionally, high amounts of oxygen and particle sintering lower H2 evolution rates by introducing defect states or grain boundaries. It was also discovered that aging mp-Si NPs under ambient conditions result in continued surface oxidation which deleteriously affects its photocatalytic performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".