Abstract: Herein, the efficacy of WOx-promoted CeO2-SiO2 and CeO2-ZrO2 mixed oxide catalysts in the solvent-free selective oxidation of benzyl alcohol to benzaldehyde using molecular oxygen as an oxidant is reported. We evaluated the effects of the oxidant and catalyst concentration, reaction duration, and temperature on the reaction with an aim to optimize the reaction conditions. The as-prepared CeO2, CeO2-ZrO2, CeO2-SiO2, WOx/CeO2, WOx/CeO2-ZrO2, and WOx/CeO2-SiO2 catalysts were characterized by X-ray diffraction (XRD), N-2 adsorption-desorption, Raman spectroscopy, temperature-programmed desorption of ammonia (TPD-NH3), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). These characterisation results indicated that the WOx/CeO2-SiO2 catalyst possessed improved physicochemical (i.e., structural, textural, and acidic) properties owing to the strong interactivity between WOx and CeO2-SiO2. A higher number of Ce3+ ions (I-u '''/I-Total) were created with the WOx/CeO2-SiO2 catalyst than those with the other catalysts in this work, indicating the generation of a high number of oxygen vacancies. The WOx/CeO2-SiO2 catalyst exhibited a high conversion of benzyl alcohol (>99%) and a high selectivity (100%) toward benzaldehyde compared to the other promoted catalysts (i.e., WOx/CeO2 and WOx/CeO2-ZrO2), which is attributed to the smaller particle size of the WOx and CeO2 and their high specific surface area, more significant number of acidic sites, and superior number of oxygen vacancies. The WOx/CeO2-SiO2 catalyst could be quickly recovered and utilized at least five times without suffering any appreciable activity loss.

Abstract: Plasma catalysis is a rapidly growing field, often employing a packed-bed dielectric barrier discharge plasma reactor. Such dielectric barrier discharges are complex, especially when a packing material (e.g., a catalyst) is introduced in the discharge volume. Catalysts are known to affect the plasma discharge, though the underlying mechanisms influencing the plasma physics are not fully understood. Moreover, the effect of the catalysts on the plasma discharge and its subsequent effect on the overall performance is often overlooked. In this work, we deliberately design and synthesize catalysts to affect the plasma discharge in different ways. These Ni or Co alumina-based catalysts are used in plasma-catalytic dry reforming of methane and ammonia synthesis. Our work shows that introducing a metal to the dielectric packing can affect the plasma discharge, and that the distribution of the metal is crucial in this regard. Further, the altered discharge can greatly influence the overall performance. In an atmospheric pressure dielectric barrier discharge reactor, this apparently more uniform plasma yields a significantly better performance for ammonia synthesis compared to the more conventional filamentary discharge, while it underperforms in dry reforming of methane. This study stresses the importance of analyzing the plasma discharge in plasma catalysis experiments. We hope this work encourages a more critical view on the plasma discharge characteristics when studying various catalysts in a plasma reactor.

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.

Abstract: Plasma‐based CO₂conversion has attracted increasing interest. However, to understand the impact of plasma operation on post‐plasma processes, we studied the effect of adding N₂, N₂/CH₄and N₂/CH₄/H₂O to a CO₂gliding arc plasmatron (GAP) to obtain valuable insights into their impact on exhaust stream composition and temperature, which will serve as feed gas and heat for post‐plasma catalysis (PPC). Adding N₂improves the CO₂conversion from 4 % to 13 %, and CH₄addition further promotes it to 44 %, and even to 61 % at lower gas flow rate (6 L/min), allowing a higher yield of CO and hydrogen for PPC. The addition of H₂O, however, reduces the CO₂conversion from 55 % to 22 %, but it also lowers the energy cost, from 5.8 to 3 kJ/L. Regarding the temperature at 4.9 cm post‐plasma, N₂addition increases the temperature, while the CO₂/CH₄ratio has no significant effect on temperature. We also calculated the temperature distribution with computational fluid dynamics simulations. The obtained temperature profiles (both experimental and calculated) show a decreasing trend with distance to the exhaust and provide insights in where to position a PPC bed.

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.

Abstract: Electrification and carbon capture technologies are essential for achieving net-zero emissions in the chemical sector. A crucial strategy involves converting captured CO2 into CO, a valuable chemical feedstock. This study evaluates the feasibility of two innovative methods: plasma activation and electrolysis, using clean electricity and captured CO2. Specifically, it compares a gliding arc plasma reactor with an embedded novel carbon bed system to a modern zero-gap type low-temperature electrolyser. The plasma method stood out with an energy cost of 19.5 GJ per tonne CO, marking a 43% reduction compared to electrolysis and conventional methods. CO production costs for plasma- and electrolysis-based plants were $671 and $962 per tonne, respectively. However, due to high uncertainty regarding electrolyser costs, the CO production costs in electrolysis-based plants may actually range from $570 to $1392 per tonne. The carbon bed system in the plasma method was a key factor in facilitating additional CO generation from O-2 and enhancing CO2 conversion, contributing to its cost-effectiveness. Challenges for electrolysis included high costs of equipment and low current densities. Addressing these limitations could significantly decrease production costs, but challenges arise from the mutual relationship between intrinsic parameters, such as CO2 conversion, CO2 input flow, or energy cost. In a future scenario with affordable feedstocks and equipment, costs could drop below $500 per tonne for both methods. While this may be more challenging for electrolysis due to complexity and expensive catalysts, plasma-based CO production appears more viable and competitive.

Abstract: Despite the recent promising potential of plasma-based nitrogen fixation, the technology faces significant challenges in efficient upscaling. To tackle this challenge, we investigate two reactors, i.e., a small one, operating in a flow rate range of 5-20 ln min-1 and current range of 200-500 mA, and a larger one, operating at higher flow rate (100-300 ln min-1) and current (400-1000 mA). Both reactors operate in a pin-to-pin configuration and are powered by direct current (DC) from the same power supply unit, to allow easy comparison and evaluate the effect of upscaling. In the small reactor, we achieve the lowest energy cost (EC) of 2.8 MJ mol-1, for a NOx concentration of 1.72%, at a flow rate of 20 ln min-1, yielding a production rate (PR) of 33 g h-1. These values are obtained in air; in oxygen-enriched air, the results are typically better, at the cost of producing oxygen-enriched air. In the large reactor, the higher flow rates reduce the NOx concentration due to lower SEI, while maintaining a similar EC. This stresses the important effect of the geometrical configuration of the arc, which is typically concentrated in the center of the reactor, resulting in limited coverage of the reacting gas flow, and this is identified as the limiting factor for upscaling. However, our experiments reveal that by changing the reactor configuration, and thus the plasma geometry and power deposition mechanisms, the amount of gas treated by the plasma can be enhanced, leading to successful upscaling. To obtain more insights in our experiments, we performed thermodynamic equilibrium calculations. First of all, they show that our measured lowest EC closely aligns with the calculated minimum thermodynamic equilibrium at atmospheric pressure. In addition, they reveal that the limited NOx production in the large reactor results from the contracted nature of the plasma. To solve this limitation, we let the large reactor operate in so-called torch configuration. Indeed, the latter enhances the NOx concentrations compared to the pin-to-pin configuration, yielding a PR of 80 g h-1 at an EC of 2.9 MJ mol-1 and NOx concentration of 0.31%. This illustrates the importance of reactor design in upscaling. With the focus on feasibility evaluation of scaling-up plasma-based nitrogen fixation by combined experiments and thermodynamic modelling, we aim to tackle the challenge of design and development of an energy-efficient and scaled-up plasma reactor.

Abstract: Oligomerized [Cu-O-Cu] species are reported to be efficient in promoting plasma catalytic one-step steam reforming of methane to methanol and hydrogen, achieving 6.8% CH4 conversion and 73.1% CH3OH selectivity without CO2.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: We studied the use of low current (hundreds of milliamperes) gliding arc/glow discharges for CO2 dissociation, at atmospheric pressure, in three different configurations. All of these are based on the gliding arc design with flat diverging electrodes. The discharge is mainly in the normal glow regime with contracted positive column. The CO2 gas is injected from a nozzle, at the closest separation between the electrodes. A pair of quartz glasses is placed on both sides of the electrodes, so that the gas flow is restricted to the active plasma area, between the electrodes. For two of the tested configurations, an external magnetic field was applied, to create a magnetic force, both in the direction of the gas flow, and opposite to the gas flow. In the first case, the arc is accelerated, shortening the period between ignition and extinction, while in the second case, it is stabilized (magneticallystabilized). We studied two quantities, namely the CO2 conversion and the energy efficiency of the conversion. Generally, the CO2 conversion decreases with increasing flow rate and increases with power. The energy effi­ciency increases with the flow rate, for all configurations. The magnetically-stabilized configuration is more stable and efficient at low gas flow rates, but has poor performance at high flow rates, while the non-stabilized configurations exhibit good conversion for a larger range of flow rates, but they are generally more unstable and less efficient.

Abstract: A pulsed microwave surfaguide discharge operating at 2.45 GHz was used for the conversion of molecular nitrogen into valuable compounds in several gas mixtures: N2 :O2 , N2 :O2 :CO2 and N2 :CO2 . The ro-vibrational absorption bands of the molecular species were monitored by a Fourier transform infrared apparatus in the post-discharge region in order to evaluate the relative number density of species, specifically NO production. The effects of specific energy input, pulse frequency, gas flow fraction, gas admixture and gas flow rate were studied for better understanding and optimization of the NO production yield and the corresponding energy cost (EC). By both the experiment and modelling, a highest NO yield is obtained at N2 :O2 (1:1) gas ratio in N2 :O2 mixture. The NO yield reveals a small growth followed by saturation when pulse repetition frequency increases. The energy efficiency start decreasing after the energy input reaches about 5 eV/molec, whereas the NO yield rises steadily at the same time. The lowest EC of about 8 MJ mol−1 corresponding to the yield and the energy efficiency of about 7% and 1% are found, respectively, in an optimum discharge condition in our case.

Abstract: The rate, selectivity and efficiency of plasma-based conversion processes is strongly affected by nonequilibrium phenomena. High concentrations of vibrationally excited molecules are such a plasma-induced effect. It is frequently assumed that vibrationally excited molecules are important in plasma catalysis because their presence lowers the apparent activation energy of dissociative chemisorption reactions and thus increases the conversion rate. A detailed atomic-level understanding of vibrationally stimulated catalytic reactions in the context of plasma catalysis is however lacking. Here, we couple a recently developed statistical model of a plasma-induced vibrational nonequilibrium to molecular dynamics simulations, enhanced sampling methods, and machine learning techniques. We quantify the impact of a vibrational nonequilibrium on the dissociative chemisorption barrier of H2 and CH4 on nickel catalysts over a wide range of vibrational temperatures. We investigate the effect of surface structure and compare the role of different vibrational modes of methane in the dissociation process. For low vibrational temperatures, very high vibrational efficacies are found, and energy in bend vibrations appears to dominate the dissociation of methane. The relative impact of vibrational nonequilibrium is much higher on terrace sites than on surface steps. We then show how our simulations can help to interpret recent experimental results, and suggest new paths to a better understanding of plasma catalysis.

Abstract: Plasma-based CO2 and CH4 conversion is gaining increasing interest, and a great portion of research is dedicated to adapting the process to actual industrial conditions. In an industrial context, the process needs to be able to process N2 admixtures, since most industrial gas emissions contain significant amounts of N2, and gas separations are financially costly. In this paper we therefore investigate the effect of N2 on the CO2 and CH4 conversion in a gliding arc plasmatron reactor. The addition of 20 % N2 reduces the energy cost of the conversion process by 21 % compared to a pure CO2/CH4 mixture, from 2.9 down to 2.2 eV/molec (or from 11.5 to 8.7 kJ/L), yielding a CO2 and CH4 (absolute) conversion of 28.6 and 35.9 % and an energy efficiency of 58 %. These results are among the best reported in literature for plasma-based DRM, demonstrating the benefits of N2 present in the mix. Compared to DRM results in different plasma reactor types, a low energy cost was achieved. To understand the underlying mechanisms of N2 addition, we developed a combination of four different computational models, which reveal that the beneficial effect of N2 addition is attributed to (i) a rise in the electron density (increasing the plasma conductivity, and therefore reducing the plasma power needed to sustain the plasma, which reduces the energy cost), as well as (ii) a rise in the gas temperature, which accelerates the CO2 and CH4 conversion reactions.