Integrated experimental and multiphysics modeling investigation of a novel photocatalytic reactor for sustainable hydrogen production
Bibliographic record
Abstract
This study presents the design, fabrication, and systematic investigation of a novel cylindrical cavity photoreactor integrated with a solar parabolic dish concentrator for photocatalytic hydrogen production through water splitting. A comprehensive research methodology combining rigorous experimental validation with advanced computational fluid dynamics (CFD) modeling is implemented to investigate the complex multiphysics phenomena governing system performance. Silver-doped TiO 2 photocatalysts with various Ag content (5–15 wt%) are synthesized and characterized, with 15 wt% Ag-TiO 2 demonstrating superior visible light absorption through surface plasmon resonance effects and achieving the highest hydrogen production rate of 6.62 mmol g −1 h −1 , 12.35 % higher than the 5 wt% formulation. A sophisticated multiphysics CFD model incorporating Monte Carlo ray-tracing, radiative transfer equations, fluid dynamics, and photocatalytic reaction kinetics is developed and validated against experimental data, exhibiting excellent agreement with mean relative errors of 1.56 % for temperature predictions and 8.37 % for hydrogen production rates. Parametric analysis reveals that positioning the reactor at 0.9 times the focal length yields optimal performance with a 16.87 % enhancement in hydrogen production compared to focal point placement. Further optimization identifies 500 mg/L as the ideal catalyst concentration, balancing enhanced photocatalytic activity with sufficient light penetration to maximize specific hydrogen production (6.31 mmol g −1 h −1 ). The system demonstrates the highest efficiency during the 11: 00–17:00 operational window, coinciding with peak solar irradiance. This integrated experimental-computational approach provides valuable insights for optimizing solar photoreactor design and advances the development of sustainable hydrogen production technologies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".