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“Mechanism of Nitrogen Fixation by Nitrogenase: The Next Stage”. Hoffman BM, Lukoyanov D, Yang Z-Y, Dean DR, Seefeldt LC, Chemical Reviews 114, 4041 (2014). http://doi.org/10.1021/cr400641x
Abstract: Ammonia is a crucial nutrient used for plant growth and as a building block in pharmaceutical and chemical industry, produced via nitrogen fixation of the ubiquitous atmospheric N2. Current industrial ammonia production relies heavily on fossil resources, but a lot of work is put into developing non-fossil based pathways. Among these is the use of nonequilibrium plasma. In this work, we investigated water vapor as H source for nitrogen fixation into NH3 by non-equilibrium plasma. The highest selectivity towards NH3 was observed with low amounts of added H2O vapor, but the highest production rate was reached at high H2O vapor.
Keywords: A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
DOI: 10.1021/cr400641x
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“Correction: From the Birkeland–Eyde process towards energy-efficient plasma-based NOXsynthesis: a techno-economic analysis”. Rouwenhorst KHR, Jardali F, Bogaerts A, Lefferts L, Energy &, Environmental Science 16, 6170 (2023). http://doi.org/10.1039/D3EE90066E
Abstract: Correction for ‘From the Birkeland–Eyde process towards energy-efficient plasma-based NO<sub><italic>X</italic></sub>synthesis: a techno-economic analysis’ by Kevin H. R. Rouwenhorst<italic>et al.</italic>,<italic>Energy Environ. Sci.</italic>, 2021,<bold>14</bold>, 2520–2534, https://doi.org/10.1039/D0EE03763J.
Keywords: A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 32.5
DOI: 10.1039/D3EE90066E
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“Plasma catalysis in ammonia production and decomposition: Use it, or lose it?”.Gorbanev Y, Fedirchyk I, Bogaerts A, Current Opinion in Green and Sustainable Chemistry 47, 100916 (2024). http://doi.org/10.1016/j.cogsc.2024.100916
Abstract: The combination of plasma with catalysis for the synthesis and decomposition of NH3 is an attractive route to the production of carbon-neutral fertiliser and energy carriers and its conversion into H2. Recent years have seen fast developments in the field of plasma-catalytic NH3 life cycle. This work summarises the most recent advances in plasma-catalytic and related NH3-focussed processes, identifies some of the most important discoveries, and addresses plausible strategies for future developments in plasma-based NH3 technology.
Keywords: A1 Journal Article; Plasma Nitrogen fixation Ammonia Plasma catalysis Production and decomposition; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 9.3
DOI: 10.1016/j.cogsc.2024.100916
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“Machine learning-driven optimization of plasma-catalytic dry reforming of methane”. Cai Y, Mei D, Chen Y, Bogaerts A, Tu X, Journal of Energy Chemistry 96, 153 (2024). http://doi.org/10.1016/j.jechem.2024.04.022
Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of 51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.
Keywords: A1 Journal Article; Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 13.1
DOI: 10.1016/j.jechem.2024.04.022
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“Improving the performance of gliding arc plasma-catalytic dry reforming via a new post-plasma tubular catalyst bed”. Xu W, Buelens LC, Galvita VV, Bogaerts A, Meynen V, Journal of CO2 Utilization 83, 102820 (2024). http://doi.org/10.1016/j.jcou.2024.102820
Abstract: A combination of a gliding arc plasmatron (GAP) reactor and a newly designed tubular catalyst bed (N-bed) was applied to investigate the post-plasma catalytic (PPC) effect for dry reforming of methane (DRM). As comparison, a traditional plasma catalyst bed (T-bed) was also utilized. The post-plasma catalytic effect of a Ni-based mixed oxide (Ni/MO) catalyst with a thermal catalytic performance of 77% CO2 and 86% CH4 conversion at 700 ℃ was studied. Although applying the T-bed had little effect on plasma based CO2 and CH4 conversion, an increase in selectivity to H2 was obtained with a maximum value of 89% at a distance of 2 cm. However, even when only α-Al2O3 packing material was used in the N-bed configuration, compared to the plasma alone and the T-bed, an increase of the CO2 and CH4 conversion from 53% and 53% to 69% and 69% to 83% was achieved. Addition of the Ni/MO catalyst further enhanced the DRM reaction, resulting in conversions of 79% for CO2 and 91% for
CH4. Hence, although no insulation nor external heating was applied to the N-bed post plasma, it provides a slightly better conversion than the thermal catalytic performance with the same catalyst, while being fully electrically driven. In addition, an enhanced CO selectivity to 96% was obtained and the energy cost was reduced from ~ 6 kJ/L (plasma alone) to 4.3 kJ/L. To our knowledge, it is the first time that a post-plasma catalytic system achieves this excellent catalytic performance for DRM without extra external heating or insulation.
Keywords: A1 Journal Article; Dry reforming Gliding arc plasma Plasma catalytic DRM Ni-based mixed oxide Post-plasma catalysis; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 7.7
DOI: 10.1016/j.jcou.2024.102820
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“Coupled multi-dimensional modelling of warm plasmas: Application and validation for an atmospheric pressure glow discharge in CO2/CH4/O2”. Maerivoet S, Tsonev I, Slaets J, Reniers F, Bogaerts A, Chemical Engineering Journal 492, 152006 (2024). http://doi.org/10.1016/j.cej.2024.152006
Abstract: To support experimental research into gas conversion by warm plasmas, models should be developed to explain the experimental observations. These models need to describe all physical and chemical plasma properties in a coupled way. In this paper, we present a modelling approach to solve the complete set of assumed relevant equations, including gas flow, heat balance and species transport, coupled with a rather extensive chemistry set, consisting of 21 species, obtained by reduction of a more detailed chemistry set, consisting of 41 species. We apply this model to study the combined CO2 and CH4 conversion in the presence of O2, in a direct current atmospheric pressure glow discharge. Our model can predict the experimental trends, and can explain why higher O2 fractions result in higher CH4 conversion, namely due to the higher gas temperature, rather than just by additional chemical reactions. Indeed, our model predicts that when more O2 is added, the energy required to reach any set temperature (i.e., the enthalpy) drops, allowing the system to reach higher temperatures with similar amounts of energy. This is in turn related to the higher H2O fraction and lower H2 fraction formed in the plasma, as demonstrated by our model. Altogether, our new self-consistent model can capture the main physics and chemistry occurring in this warm plasma, which is an important step towards predictive modelling for plasma-based gas conversion.
Keywords: A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 15.1
DOI: 10.1016/j.cej.2024.152006
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“Improving Molecule–Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2Hydrogenation”. Cai Y, Michiels R, De Luca F, Neyts E, Tu X, Bogaerts A, Gerrits N, The Journal of Physical Chemistry C 128, 8611 (2024). http://doi.org/10.1021/acs.jpcc.4c01110
Abstract: Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks.
Keywords: A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 3.7
DOI: 10.1021/acs.jpcc.4c01110
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