Utilizing full-spectrum sunlight for ammonia decomposition to hydrogen over GaN nanowires-supported Ru nanoparticles on silicon
Bibliographic record
Abstract
Photo-thermal-coupling ammonia decomposition presents a promising strategy for utilizing the full-spectrum to address the H2 storage and transportation issues. Herein, we exhibit a photo-thermal-catalytic architecture by assembling gallium nitride nanowires-supported ruthenium nanoparticles on a silicon for extracting hydrogen from ammonia aqueous solution in a batch reactor with only sunlight input. The photoexcited charge carriers make a predomination contribution on H2 activity with the assistance of the photothermal effect. Upon concentrated light illumination, the architecture significantly reduces the activation energy barrier from 1.08 to 0.22 eV. As a result, a high turnover number of 3,400,750 is reported during 400 h of continuous light illumination, and the H2 activity per hour is nearly 1000 times higher than that under the pure thermo-catalytic conditions. The reaction mechanism is extensively studied by coordinating experiments, spectroscopic characterizations, and density functional theory calculation. Outdoor tests validate the viability of such a multifunctional architecture for ammonia decomposition toward H2 under natural sunlight. The author report a Ru NPs/GaN NWs nanoarchitecture on Si for utilizing full spectrum to drive efficient and robust photothermal H2 production from NH3 with a high turnover number of >3,400,750 over 400 h.
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 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) | 0.001 | 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".