Abstract: Nowadays, lasers are commonly applied in spectrochemical analysis methods, for sampling, plasma formation or a combination of both. Despite the numerous investigations that have been performed on these applications, the underlying processes are still insufficiently understood. In order to fasten progress in the field and in honor of the lifework of professor Rick Russo, we here provide a brief overview of the fundamental mechanisms in lasermatter interaction as proposed in literature, and throw the spotlight on some aspects that have not received much attention yet. For an organized discussion, we choose laser ablation, laser desorption and the associated gaseous plasma formation as the central processes in this perspective article, based on a classification of the laserbased spectrochemical analysis techniques and the corresponding laser-matter interaction regimes. First, we put the looking glass over the excitation and thermalization mechanisms in the laser-irradiated condensed phase, for which we propose the so-called multi-plasma model. This novel model can be understood as an extension of the well-known two-temperature model, featuring multiple thermodynamic dimensions, each of which corresponds to a quasi-particle type. Next, the focus is placed on the mass transfer and ionization mechanisms, after which we shortly highlight the possible role of anisotropic and magnetic effects in the laser-excited material. We hope this perspective article motivates more fundamental research on laser-matter interaction, as a continuation of the lifework of Rick Russo.

Abstract: Immobilization of two single transition metal (TM) atoms on a substrate host opens numerous possibilities for catalyst design. If the substrate contains more than one vacancy site, the combination of TMs along with their distribution patterns becomes a design parameter potentially complementary to the substrate itself and the bi-metal composition. By means of DFT calculations, we modeled three dissimilar bi-metal atoms (Ti, Mn, and Cu) doped into the six porphyrin-like cavities of porous C24N24 fullerene, considering different bi-metal distribution patterns for each binary complex, viz. TixCuz@C24N24, TixMny@C24N24, and MnyCuz@C24N24 (with x, y, z = 0-6). We elucidate whether controlling the distribution of bi-metal atoms into the C24N24 cavities can alter their catalytic activity toward CO2, NO2, H-2, and N-2 gas capture. Interestingly, Ti2Mn4@C24N24 and Ti2Cu4@C24N24 complexes showed the highest activity and selectively toward gas capture. Our findings provide useful information for further design of novel few-atom carbon-nitride-based catalysts.

Abstract: The treatment of a well plate by an atmospheric pressure plasma jet is common for<italic>in vitro</italic>plasma medicine research. Here, reactive species are largely produced through the mixing of the jet effluent with the surrounding atmosphere. This mixing can be influenced not only by the ambient conditions, but also by the geometry of the treated well. To limit this influence and control the atmosphere, a shielding gas is sometimes applied. However, the interplay between the gas shield and the well geometry has not been investigated. In this work, we developed a 2D-axisymmetric computational fluid dynamics model of the kINPen plasma jet, to study the mixing of the jet effluent with the surrounding atmosphere, with and without gas shield. Our computational and experimental results show that the choice of well type can have a significant influence on the effluent conditions, as well as on the effectiveness of the gas shield. Furthermore, the geometry of the shielding gas device can substantially influence the mixing as well. Our results provide a deeper understanding of how the choice of setup geometry can influence the plasma treatment, even when all other operating parameters are unchanged.

Abstract: The propagation of a short laser beam in plasma is investigated analytically Relativistic ponderomotive force and space charge effects are included, and an equation describing the evolution of the laser spot size is derived. It is shown that self-consistent electron cavitation can lead to self channelling and thus long-distance self-focusing of the laser. The condition for the latter to occur is given.

Abstract: Non-equilibrium inductively coupled plasmas (ICPs) operating in hydrogen are of significant interest for applications including large-area materials processing. Increasing control of spatial gas heating, which drives the formation of neutral species density gradients and the rate of gas-temperature-dependent reactions, is critical. In this study, we use 2D fluid-kinetic simulations with the Hybrid Plasma Equipment Model to investigate the spatially resolved production of atomic hydrogen in a low-pressure planar ICP operating in pure hydrogen (10-20 Pa or 0.075-0.15 Torr, 300 W). The reaction set incorporates self-consistent calculation of the spatially resolved gas temperature and 14 vibrationally excited states. We find that the formation of neutral-gas density gradients, which result from spatially non-uniform electrical power deposition at constant pressure, can drive significant variations in the vibrational distribution function and density of atomic hydrogen when gas heating is spatially resolved. This highlights the significance of spatial gas heating on the production of reactive species in relatively high-power-density plasma processing sources.

Abstract: Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This article presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various applications of plasma in hygiene, decontamination, and medicine, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come.

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.