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: In recent years there has been growing interest in the use of low-temperature atmospheric pressure plasmas for biomedical applications. Currently, however, there is very little fundamental knowledge regarding the relevant interaction mechanisms of plasma species with living cells. In this paper, we investigate the interaction of important plasma species, such as O3, O2 and O atoms, with bacterial peptidoglycan (or murein) by means of reactive molecular dynamics simulations. Specifically, we use the peptidoglycan structure to model the gram-positive bacterium Staphylococcus aureus murein. Peptidoglycan is the outer protective barrier in bacteria and can therefore interact directly with plasma species. Our results demonstrate that among the species mentioned above, O3 molecules and especially O atoms can break important bonds of the peptidoglycan structure (i.e. CO, CN and CC bonds), which subsequently leads to the destruction of the bacterial cell wall. This study is important for gaining a fundamental insight into the chemical damaging mechanisms of the bacterial peptidoglycan structure on the atomic scale.

Abstract: A Monte Carlo model has been developed for investigating the electron behavior in a dual-magnetron sputter deposition system. To describe the three-dimensional (3D) geometry, different reference frames, i.e. a local and a global coordinate system, were used. In this study, the influence of both closed and mirror magnetic field configurations on the plasma properties is investigated. In the case of a closed magnetic field configuration, the calculated electron trajectories show that if an electron is emitted in (or near) the center of the cathode, where the influence of the magnetic field is low, it is able to travel from one magnetron to the other. On the other hand, when an electron is created at the race track area, it is more or less trapped in the strong magnetic field and cannot easily escape to the second magnetron region. In the case of a mirror magnetic field configuration, irrespective of where the electron is emitted from the cathode, it cannot travel from one magnetron to the other because the magnetic field lines guide the electron to the substrate. Moreover, the electron density and electron impact ionization rate have been calculated and studied in detail for both configurations.

Abstract: Understanding the fundamental dynamics in carbon nanotube (CNT) catalysts is of primary importance to understand CNT nucleation. This Letter reports on calculated bond switching (BS) rates in pure and carbon containing nickel nanoclusters. The rates are analyzed in terms of their temperature dependent spatial distribution and the mobility of the cluster atoms. The BS mechanism is found to change from vibrational to diffusional at around 900 K, with a corresponding strong increase in activation energy. Furthermore, the BS activation energy is observed to decrease as the carbon content in the cluster increases, resulting in an effective liquification of the cluster.

Abstract: Plasma technology is gaining increasing interest for the splitting of CO2 into CO and O2. We have performed experiments to study this process in a dielectric barrier discharge (DBD) plasma with a wide range of parameters. The frequency and dielectric material did not affect the CO2 conversion and energy efficiency, but the discharge gap can have a considerable effect. The specific energy input has the most important effect on the CO2 conversion and energy efficiency. We have also presented a plasma chemistry model for CO2 splitting, which shows reasonable agreement with the experimental conversion and energy efficiency. This model is used to elucidate the critical reactions that are mostly responsible for the CO2 conversion. Finally, we have compared our results with other CO2 splitting techniques and we identified the limitations as well as the benefits and future possibilities in terms of modifications of DBD plasmas for greenhouse gas conversion in general.

Abstract: The growth mechanism and chirality formation of a single-walled carbon nanotube (SWNT) on a surface-bound nickel nanocluster are investigated by hybrid reactive molecular dynamics/force-biased Monte Carlo simulations. The validity of the interatomic potential used, the so-called ReaxFF potential, for simulating catalytic SWNT growth is demonstrated. The SWNT growth process was found to be in agreement with previous studies and observed to proceed through a number of distinct steps, viz., the dissolution of carbon in the metallic particle, the surface segregation of carbon with the formation of aggregated carbon clusters on the surface, the formation of graphitic islands that grow into SWNT caps, and finally continued growth of the SWNT. Moreover, it is clearly illustrated in the present study that during the growth process, the carbon network is continuously restructured by a metal-mediated process, thereby healing many topological defects. It is also found that a cap can nucleate and disappear again, which was not observed in previous simulations. Encapsulation of the nanoparticle is observed to be prevented by the carbon network migrating as a whole over the cluster surface. Finally, for the first time, the chirality of the growing SWNT cap is observed to change from (11,0) over (9,3) to (7,7). It is demonstrated that this change in chirality is due to the metal-mediated restructuring process.