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: Atomic scale simulations of the nucleation and growth of carbon nanotubes is essential for understanding their growth mechanism. In spite of over twenty years of simulation efforts in this area, limited progress has so far been made on addressing the role of the hydrocarbon growth precursor. Here we report on atomic scale simulations of cap nucleation of single-walled carbon nanotubes from hydrocarbon precursors. The presented mechanism emphasizes the important role of hydrogen in the nucleation process, and is discussed in relation to previously presented mechanisms. In particular, the role of hydrogen in the appearance of unstable carbon structures during in situ experimental observations as well as the initial stage of multi-walled carbon nanotube growth is discussed. The results are in good agreement with available experimental and quantum-mechanical results, and provide a basic understanding of the incubation and nucleation stages of hydrocarbon-based CNT growth at the atomic level.

Abstract: This study reports on the possible effects of OH radical impact on the transmembrane domain 6 of P-glycoprotein, TM6, which plays a crucial role in drug binding in human cells. For the first time, we employ molecular dynamics (MD) simulations based on the self-consistent charge density functional tight binding (SCC-DFTB) method to elucidate the potential sites of fragmentation and mutation in this domain upon impact of OH radicals, and to obtain fundamental information about the underlying reaction mechanisms. Furthermore, we apply non-reactive MD simulations to investigate the long-term effect of this mutation, with possible implications for drug binding. Our simulations indicate that the interaction of OH radicals with TM6 might lead to the breaking of C-C and C-N peptide bonds, which eventually cause fragmentation of TM6. Moreover, according to our simulations, the OH radicals can yield mutation in the aromatic ring of phenylalanine in TM6, which in turn affects its structure. As TM6 plays an important role in the binding of a range of cytotoxic drugs with P-glycoprotein, any changes in its structure are likely to affect the response of the tumor cell in chemotherapy. This is crucial for cancer therapies based on reactive oxygen species, such as plasma treatment.

Abstract: We have computationally investigated the introduction of copper elemental particles in an inductively coupled plasma torch connected to a sampling cone, including for the first time the ionization of the sample. The sample is inserted as liquid particles, which are followed inside the entire torch, i.e., from the injector inlet up to the ionization and reaching the sampler. The spatial position of the ion clouds inside the torch as well as detailed information on the copper species fluxes at the position of the sampler orifice and the exhausts of the torch are provided. The effect of on- and off-axis injection is studied. We clearly show that the ion clouds of on-axis injected material are located closer to the sampler with less radial diffusion. This guarantees a higher transport efficiency through the sampler cone. Moreover, our model reveals the optimum ranges of applied power and flow rates, which ensure the proper position of ion clouds inside the torch, i.e., close enough to the sampler to increase the fraction that can enter the mass spectrometer and with minimum loss of material toward the exhausts as well as a sufficiently high plasma temperature for efficient ionization.