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“Thermochemical conversion of coal and biomass blends in a top-lit updraft fixed bed reactor : experimental assessment of the ignition front propagation velocity”. Quintero-Coronel DA, Lenis-Rodas YA, Corredor LA, Perreault P, Gonzalez-Quiroga A, Energy 220, 119702 (2021). http://doi.org/10.1016/J.ENERGY.2020.119702
Abstract: Co-thermochemical conversion of coal and biomass can potentially decrease the use of fossil carbon and pollutant emissions. This work presents experimental results for the so-called top-lit updraft fixed bed reactor, in which the ignition front starts at the top and propagates downward while the gas product flows upwards. The study focuses on the ignition front propagation velocity for the co-thermochemical conversion of palm kernel shell and high-volatile bituminous coal. Within the range of assessed air superficial velocities, the process occurred under gasification and near stoichiometric conditions. Under gasification conditions increasing coal particle size from 7.1 to 22 mm decreased ignition front velocity by around 26% regardless of the coal volume percentage. Furthermore, increasing coal volume percentage and decreasing coal particle size result in product gas with higher energy content. For the operation near stoichiometric conditions, increasing coal volume percentage from 10 to 30% negatively affected the ignition front velocity directly proportional to its particle size. Additional experiments confirmed a linear dependence of ignition front velocity on air superficial velocity. Further steps in the development of the top-lit updraft technology are implementing continuous solids feeding and variable cross-sectional area and optimizing coal particle size distribution.
Keywords: A1 Journal article; Engineering sciences. Technology; Sustainable Energy, Air and Water Technology (DuEL)
Impact Factor: 4.52
DOI: 10.1016/J.ENERGY.2020.119702
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“Understanding the Activation of Anionic Redox Chemistry in Ti4+-Substituted Li2MnO3as a Cathode Material for Li-Ion Batteries”. Paulus A, Hendrickx M, Mayda S, Batuk M, Reekmans G, von Holst M, Elen K, Abakumov AM, Adriaensens P, Lamoen D, Partoens B, Hadermann J, Van Bael MK, Hardy A, ACS applied energy materials 6, 6956 (2023). http://doi.org/10.1021/acsaem.3c00451
Abstract: Layered Li-rich oxides, demonstrating both cationic and anionic redox chemistry being used as positive electrodes for Li-ion batteries,have raised interest due to their high specific discharge capacities exceeding 250 mAh/g. However, irreversible structural transformations triggered by anionic redox chemistry result in pronounced voltagefade (i.e., lowering the specific energy by a gradual decay of discharge potential) upon extended galvanostatic cycling. Activating or suppressing oxygen anionic redox through structural stabilization induced by redox-inactivecation substitution is a well-known strategy. However, less emphasishas been put on the correlation between substitution degree and theactivation/suppression of the anionic redox. In this work, Ti4+-substituted Li2MnO3 was synthesizedvia a facile solution-gel method. Ti4+ is selected as adopant as it contains no partially filled d-orbitals. Our study revealedthat the layered “honeycomb-ordered” C2/m structure is preserved when increasing the Ticontent to x = 0.2 in the Li2Mn1-x Ti (x) O-3 solidsolution, as shown by electron diffraction and aberration-correctedscanning transmission electron microscopy. Galvanostatic cycling hintsat a delayed oxygen release, due to an improved reversibility of theanionic redox, during the first 10 charge-discharge cyclesfor the x = 0.2 composition compared to the parentmaterial (x = 0), followed by pronounced oxygen redoxactivity afterward. The latter originates from a low activation energybarrier toward O-O dimer formation and Mn migration in Li2Mn0.8Ti0.2O3, as deducedfrom first-principles molecular dynamics (MD) simulations for the“charged” state. Upon lowering the Ti substitution to x = 0.05, the structural stability was drastically improvedbased on our MD analysis, stressing the importance of carefully optimizingthe substitution degree to achieve the best electrochemical performance.
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Condensed Matter Theory (CMT)
Impact Factor: 6.4
DOI: 10.1021/acsaem.3c00451
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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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“Feasibility study of a small-scale fertilizer production facility based on plasma nitrogen fixation”. Manaigo F, Rouwenhorst K, Bogaerts A, Snyders R, Energy Conversion and Management 302, 118124 (2024). http://doi.org/10.1016/j.enconman.2024.118124
Keywords: A1 Journal Article; Plasma-based nitrogen fixation Haber-Bosch Feasibility study Fertilizer production; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 10.4
DOI: 10.1016/j.enconman.2024.118124
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“Is a catalyst always beneficial in plasma catalysis? Insights from the many physical and chemical interactions”. Loenders B, Michiels R, Bogaerts A, Journal of Energy Chemistry 85, 501 (2023). http://doi.org/10.1016/j.jechem.2023.06.016
Abstract: Plasma-catalytic dry reforming of CH4 (DRM) is promising to convert the greenhouse gasses CH4 and CO2 into value-added chemicals, thus simultaneously providing an alternative to fossil resources as feedstock for the chemical industry. However, while many experiments have been dedicated to plasma-catalytic DRM, there is no consensus yet in literature on the optimal choice of catalyst for targeted products, because the underlying mechanisms are far from understood. Indeed, plasma catalysis is very complex, as it encompasses various chemical and physical interactions between plasma and catalyst, which depend on many parameters. This complexity hampers the comparison of experimental results from different studies, which, in our opinion, is an important bottleneck in the further development of this promising research field. Hence, in this perspective paper, we describe the important physical and chemical effects that should be accounted for when designing plasma-catalytic experiments in general, highlighting the need for standardized experimental setups, as well as careful documentation of packing properties and reaction conditions, to further advance this research field. On the other hand, many parameters also create many windows of opportunity for further optimizing plasma-catalytic systems. Finally, various experiments also reveal the lack of improvement in plasma catalysis compared to plasma-only, specifically for DRM, but the underlying mechanisms are unclear. Therefore, we present our newly developed coupled plasma-surface kinetics model for DRM, to provide more insight in the underlying reasons. Our model illustrates that transition metal catalysts can adversely affect plasmacatalytic DRM, if radicals dominate the plasma-catalyst interactions. Thus, we demonstrate that a good understanding of the plasma-catalyst interactions is crucial to avoiding conditions at which these interactions negatively affect the results, and we provide some recommendations for improvement. For instance, we believe that plasma-catalytic DRM may benefit more from higher reaction temperatures, at which vibrational excitation can enhance the surface reactions.
Keywords: A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 13.1
DOI: 10.1016/j.jechem.2023.06.016
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“Plasma-based CO2 conversion: How to correctly analyze the performance?”.Wanten B, Vertongen R, De Meyer R, Bogaerts A, Journal of Energy Chemistry 86, 180 (2023). http://doi.org/10.1016/j.jechem.2023.07.005
Keywords: A1 journal article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 13.1
DOI: 10.1016/j.jechem.2023.07.005
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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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