Correlating porosity with water-quality tolerance for practical on-demand hydrogen generation using porous silicon
Bibliographic record
Abstract
Moving away from fossil fuels demands practical methods to generate hydrogen (H 2 ) when needed. While large-scale H 2 production technologies continue to advance, portable and field-deployable systems remain underdeveloped. Porous silicon (p-Si), which generates H 2 when reacting with alkaline water, offers a promising route; however, the effect of its physical structure on performance under realistic conditions remains poorly understood. This study systematically examines p-Si materials synthesized via magnesiothermic reduction at varying heating rates to link crystallinity, surface area, and pore size with H 2 generation efficiency across diverse natural water sources (ocean, river, lake, well, and rainwater). The results reveal that p-Si with pore sizes below 10 nm and surface areas above 100 m 2 g −1 are critical for sustaining high H 2 yields at a fast rate, in impure water sources. These findings establish a clear structure–property relationship that advances the design of robust, on-demand chemical H 2 storage materials for real-world applications. • Water quality can adversely affect the performance of H 2 production with porous silicon. • Adjusting the physical properties of porous silicon enhances its tolerance to impure water. • For high performance in non-pure water sources, porous silicon requires pores smaller than 10 nm. • This finding enhances the practical use of porous silicon for real-world H 2 storage.
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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 | 0.001 |
| 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) | 0.001 | 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".