The influence of hydrofluoric acid etching processes on the photocatalytic hydrogen evolution reaction using mesoporous silicon nanoparticles
Bibliographic record
Abstract
H2 has been identified as one of the potential energy vectors that can provide a sustainable energy supply when produced through solar-driven water-splitting reaction. Si is the second most abundant element in the Earth's crust and can absorb a significant fraction of the solar spectrum while presenting little toxicity risk, making it an attractive material for photocatalytic H2 production. Hydrogen-terminated mesoporous Si (mp-Si) nanoparticles can be utilized to effectively drive the hydrogen evolution reaction using UV-to-visible light. In this work, the response of the photocatalytic activity of mp-Si nanoparticles to a series of HF acid treatments was investigated. A two-step magnesiothermic reduction method was used to prepare crystalline mp-Si nanoparticles with a specific surface area of 573 m2 g-1. The HF etching process was optimized as a function of the amount of acid added and the reaction time. The reaction time did not influence the H2 evolution rate substantially. However, the amount of HF used did have a significant effect on the photocatalytic activity. In the presence of ≥1.0 mL HF acid per 0.010 g of Si, morphological damage was observed using electron microscopy. N2 adsorption measurements indicated that the pore size and surface area were also altered. Solution-phase 19F{1H} NMR studies indicated the formation of SiF5- and SiF62- when larger volumes of HF were used. Both factors, morphological damage and the presence of byproducts in the pores, likely result in a lowering of the photocatalytic H2 evolution rate. Under the optimized HF treatment conditions (0.5 mL of HF per 0.010 g of Si), a H2 evolution rate of 1398 ± 30 μmol g-1 h-1 was observed.
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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".