Surface-induced gas-phase redistribution effects in plasma-catalytic dry reforming of methane: numerical investigation by fluid modeling

Abstract

Plasma catalysis is an emerging process electrification technology for industry decarbonization. Plasma-catalytic dry reforming of methane (DRM) relies on the mutual effects of the plasma and the catalyst leading to the higher chemical conversion efficiency. The effects of catalyst surfaces on the plasma are predicted to play a major role, yet they remain unexplored. Here, a 1D plasma fluid model combined with 0D surface kinetics is developed to reveal how the surface reactions on platinum (Pt) catalyst affect the redistribution of the gas-phase particles. Two contrasting models with and without the surface kinetics as well as the Spearman rank correlation coefficients are used to quantify the effect of the key species (H, CH, CH2) on the CO generation. Advancing the common knowledge that Pt catalyst can influence the plasma chemistry directly by changing the surface loss/production of particles, this study reveals that the catalyst can also affect the spatial distributions of active species, thereby influencing the plasma chemistry in an indirect way. This result goes beyond the existing state-of-the-art which commonly relies on over-simplified 0D models which cannot resolve the spatial distribution. Further analysis indicates that the species spatial redistribution is driven by the dynamic catalyst surface adsorption-desorption processes. This work enables the previously elusive account of active species redistribution and may open new opportunities for plasma-catalytic sustainable chemical processes.

Plasma Catalysis for Hydrogen Production: A Bright Future for Decarbonization

“…225 This study highlighted that the catalyst could affect the spatial distributions of active species, thereby influencing the plasma chemistry indirectly. 225 An experimental study from the Nozaki group 207,226 investigated the electronic changes in Ni/Al 2 O 3 for DRM. As shown in Figure 19, the Ni was partially oxidized to NiO up to a 20 μm depth after CO 2 plasma treatment, due to the strong oxidation ability of electron-impact excited CO 2 and NTP heating effects via a plasma-mediated Eley−Rideal mechanism.…”

“…However, the production of vibrationally excited CH 4 and CO 2 is the dominant reaction pathway at E/ N < 700 Td, compared to the dissociation and ionization rates (see Figure 17). 207 Zhu et al 225 investigated the surface-induced gas-phase redistribution in plasma-catalytic DRM using 1D plasma fluid model combined with 0D surface kinetics. The authors reported that surface reaction changes the development of electron avalanches in the plasma phase, resulting in an improved spatial inhomogeneity.…”

Numerical modeling of gas‐phase reactions of tetraethoxysilane/O2/Ar atmospheric dielectric barrier discharge for deposition

“…This is mainly due to the lack of gas-phase reaction products and their surface reaction rates, which limit further studies on surface reactions. Furthermore, it has been shown that the catalytic Al O 2 3 surface does not affect the product density by more than 10% [92] even in packed-bed DBD, which is When the input power is 500 mW, the voltage and current waveforms of TEOS/Ar discharge without O 2 are shown in Figure 3a. The discharge is stable and repeatable, and the discharge occurs at the rising edge of the pulse voltage.…”

“…This is mainly due to the lack of gas‐phase reaction products and their surface reaction rates, which limit further studies on surface reactions. Furthermore, it has been shown that the catalytic surface does not affect the product density by more than 10% [ 92 ] even in packed‐bed DBD, which is equivalent to a 0.3 mm gap. The catalyst has more active sites than raw .…”

CO2/CH4 glow discharge plasma. Part I: Experimental and numerical study of the reaction pathways

“…The CO 2 -CH 4 NRP were modelled with 1D Ćuid model in [22], whose kinetic scheme was modiĄed in [23] to take into account surface processes. Another 1D Ćuid model taking into account catalytic surfaces and spatial inhomogeneity has been developed in [24] to model a cylindrical packed-bed DBD reactor. Finally, [25] recently developed an neural network based model of a CO 2 -CH 4 nanosecond pulsed dielectric barrier discharge to predict the conversion, energy efficiency and selectivity of the discharge, but does not relies on a chemistry set.…”