H2 Photoproduction Efficiency: Implications of the Reaction Mechanism as a Function of the Methanol/Water Mixture

Barba-Nieto, Irene; Colón, G.; Kubacka, Anna; Fernández-García, Marcos

doi:10.3390/catal12040402

Cited by 1 publication

(1 citation statement)

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“…Validated CFD models and trustable experimental data could complement each other, assisting in the simulation of photoreactors for hydrogen production, using optimized QYs and PTEFs. Once this laboratory phase is completed, reactor scale-up could be addressed via CFD with a reliable kinetic-radiation model [126][127][128]. CFD would provide flow patterns, radiation and chemical species distributions and reactor performance (see figure 13(b)).…”

Section: Computational Fluid Dynamics (Cfd) and Machine Learning (Ml)mentioning

confidence: 99%

2023 roadmap on photocatalytic water splitting

et al. 2023

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As a consequence of the issues resulting from global climate change many nations are starting to transition to being low or net zero carbon economies. To achieve this objective practical alternative fuels are urgently required and hydrogen gas is deemed one of the most desirable substitute fuels to traditional hydrocarbons. A significant challenge, however, is obtaining hydrogen from sources with low or zero carbon footprint i.e. so called “green” hydrogen. Consequently, there are a number of strands of research into processes that are practical techniques for the production of this “green” hydrogen. Over the past five decades there has been a significant body of research into photocatalytic/photoelectrocatalytic processes for hydrogen production through water splitting or water reduction. There have, however been significant issues faced in terms of the practical capability of this promising technology to produce hydrogen at scale. This road map article explores a range of issues related to both photocatalytic and photoelectrocatalytic hydrogen generation ranging from basic processes, materials science through to reactor engineering and applications for biomass reforming.

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Section: Computational Fluid Dynamics (Cfd) and Machine Learning (Ml)mentioning

confidence: 99%