Abstract: Sensitive multi-element analysis techniques were applied to water samples collected in the vicinity of a small Zn-Pb-Cu sulfide mineralization in the region of Menez-Albot (Finistère, France). The variation of the trace-element content along a local stream shows the presence of the mineralization, mainly through a simultaneous positive anomaly in solution for a group of about 10 elements (e.g. Ni, Cu, Zn, As, Sb) which are connected with this type of mineralization. The anomaly decreases steeply due to the influx of swamp water rich in Fe, Mn and organic matter. The precipitation barrier is reflected in the stream sediment composition. Contamination from fertilizers was observed in some samples.

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.

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: The electrochemical behavior of some of the most relevant endocrine-disrupting phenols using unmodified carbon screen-printed electrodes (SPEs) is described for the first time. Experiments were made to assess the electrochemical behavior of phenol (PHOH), pentachlorophenol (PCP), 4-tert octylphenol (OP) and bisphenol A (BPA) and their determination in the most favorable conditions, using voltammetric methods such as cyclic voltammetry (CV), linear sweep voltammetry (LSV) and square wave voltammetry (SWV) in Britton Robinson (BR) buffer. Further, the usefulness of the electrochemical approach was validated with real samples from a local river and was compared to commercial phenols test kit, which is commonly used for on-site screening in industrial streams and wastewaters. Finally, the approach was compared with a lab-bench standard method using real samples, i.e., high-performance liquid chromatography with a photodiode array detector (HPLC-DAD).

Abstract: Uniform acceptance force biased Monte Carlo (UFMC) simulations have previously been shown to be a powerful tool to simulate atomic scale processes, enabling one to follow the dynamical path during the simulation. In this contribution, we present a simple proof to demonstrate that this uniform acceptance still complies with the condition of detailed balance, on the condition that the characteristic parameter lambda = 1/2 and that the maximum allowed step size is chosen to be sufficiently small. Furthermore, the relation to Metropolis Monte Carlo (MMC) is also established, and it is shown that UFMC reduces to MMC by choosing the characteristic parameter lambda = 0 [Rao, M. et al. Mol. Phys. 1979, 37, 1773]. Finally, a simple example compares the UFMC and MMC methods.

Abstract: We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities.spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.

Abstract: Atomistic simulation methods for the quantification of free energies are in wide use. These methods operate by sampling the probability density of a system along a small set of suitable collective variables (CVs), which is, in turn, expressed in the form of a free energy surface (FES). This definition of the FES can capture the relative stability of metastable states but not that of the transition state because the barrier height is not invariant to the choice of CVs. Free energy barriers therefore cannot be consistently computed from the FES. Here, we present a simple approach to calculate the gauge correction necessary to eliminate this inconsistency. Using our procedure, the standard FES as well as its gauge-corrected counterpart can be obtained by reweighing the same simulated trajectory at little additional cost. We apply the method to a number of systems—a particle solvated in a Lennard-Jones fluid, a Diels–Alder reaction, and crystallization of liquid sodium—to demonstrate its ability to produce consistent free energy barriers that correctly capture the kinetics of chemical or physical transformations, and discuss the additional demands it puts on the chosen CVs. Because the FES can be converged at relatively short (sub-ns) time scales, a free energy-based description of reaction kinetics is a particularly attractive option to study chemical processes at more expensive quantum mechanical levels of theory.

Abstract: We calculate bubble nucleation rates in a Lennard-Jones fluid through explicit molecular dynamics simulations. Our approach-based on a recent free energy method (dubbed reweighted Jarzynski sampling), transition state theory, and a simple recrossing correction-allows us to probe a fairly wide range of rates in several superheated and cavitation regimes in a consistent manner. Rate predictions from this approach bridge disparate independent literature studies on the same model system. As such, we find that rate predictions based on classical nucleation theory, direct brute force molecular dynamics simulations, and seeding are consistent with our approach and one another. Published rates derived from forward flux sampling simulations are, however, found to be outliers. This study serves two purposes: First, we validate the reliability of common modeling techniques and extrapolation approaches on a paradigmatic problem in materials science and chemical physics. Second, we further test our highly generic recipe for rate calculations, and establish its applicability to nucleation processes.

Abstract: Binding of the SARS-CoV-2 S-glycoprotein to cell receptors is vital for the entry of the virus into cells and subsequent infection. ACE2 is the main cell receptor for SARS-CoV-2, which can attach to the C-terminal receptor-binding domain (RBD) of the SARS-CoV-2 S-glycoprotein. The GRP78 receptor plays an anchoring role, which attaches to the RBD and increases the chance of other RBDs binding to ACE2. Although high levels of reactive oxygen and nitrogen species (RONS) are produced during viral infections, it is not clear how they affect the RBD structure and its binding to ACE2 and GRP78. In this research, we apply molecular dynamics simulations to study the effect of oxidation of the highly reactive cysteine (Cys) amino acids of the RBD on its binding to ACE2 and GRP78. The interaction energy of both ACE2 and GRP78 with the whole RBD, as well as with the RBD main regions, is compared in both the native and oxidized RBDs. Our results show that the interaction energy between the oxidized RBD and ACE2 is strengthened by 155 kJ/mol, increasing the binding of the RBD to ACE2 after oxidation. In addition, the interaction energy between the RBD and GRP78 is slightly increased by 8 kJ/mol after oxidation, but this difference is not significant. Overall, these findings highlight the role of RONS in the binding of the SARS-CoV-2 S-glycoprotein to host cell receptors and suggest an alternative mechanism by which RONS could modulate the entrance of viral particles into the cells.

Abstract: In this paper electrodeposition is used to obtain Cu nanoparticles, as it allows good control over particle size and distribution. These Cu particles were deposited onto a gas diffusion electrode which increased the resulting surface area. Prior to deposition, the surface was pre-treated with NaOH, HNO3, MQ and TX100 to investigate the influence on the electrodeposition of Cu on the gas diffusion electrode (GDE). When using HNO3, the smallest particles with the most homogeneous distribution and high particle roughness were obtained. Once the optimal substrate was determined, we further demonstrated that by altering the electrodeposition parameters, the particle size and density could be tuned. On the one hand, increasing the nucleation potential led to a higher particle density resulting in smaller particles because of an increased competition between particles. Finally, the Cu particle size increased when applying a greater growth charge and growth potential. This fundamental study thus opens up a path towards the synthesis of supported Cu materials with increased surface areas, which is interesting from a catalytic point of view. Larger surface areas are generally correlated with a better catalyst performance and thus higher product yields. This research can contributed in obtaining new insides into the deposition of metallic nanoparticles on rough surfaces. [GRAPHICS] .