Silicon Nanowire-Supported Catalysts for the Photocatalytic Reduction of Carbon Dioxide
Bibliographic record
Abstract
The conversion of carbon dioxide, a greenhouse gas, into valuable chemicals using sunlight is highly significant technologically and holds the promise of providing a more sustainable alternative to fossil fuels. To effectively utilize the abundant solar irradiation, it is essential to develop catalysts that can absorb a significant portion of the solar spectrum, particularly in the UV, visible, and infrared regions. Silicon nanowire arrays grown on silicon substrates meet this criterion, as they can absorb over 85% of solar irradiation and show minimal reflective losses across the UV, visible, and infrared portions of the solar spectrum. Herein, we report the deposition of various catalysts, including iron oxyhydroxides, copper, nickel, and ruthenium, on silicon nanowires using different catalyst deposition techniques. The photocatalytic reduction of carbon dioxide was evaluated using these catalysts. The results show that silicon nanowires coated with nickel and ruthenium oxide had the highest activity towards the photocatalytic reduction of carbon dioxide, with photomethanation rates reaching 546 μmolgcat−1h−1 for RuO2@SiNWs and 278 μmolgcat−1h−1 for Ni/NiO@SiNWs. Continued improvement of photocatalysts using nanostructured silicon supports could enable the development of solar refineries for converting gas-phase CO2 into value-added chemicals and fuels.
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".