Abstract: Natural deep eutectic solvents (NADES) show great promise as media for enzymatic reactions in areas where (bio)compatibility with natural or medicinal products is a must. While in theory they can be tailored to the intended reaction to ensure optimized yields, the knowledge to date is predominantly empirical, with some mechanistic reports providing a fragmented view at best. Therefore, it is not easy to explain experimental observations, let alone make predictions. The aim of this study was to develop a structured, holistic understanding of the effects of NADES media on enzymatic reactions, distinguishing between effects on solubility, solvation, viscosity, inhibition and denaturation. Experimental and computational chemistry methods were combined to separately study the interactions between enzyme, substrate, and NADES as reaction media. The initial enzyme activity and the final conversion of vinyl laurate transesterification by immobilized Candida antarctica lipase were studied experimentally. The direct effect of NADES on the same enzyme was modeled by molecular dynamics simulation. The effect of solubility was studied by both experimental and computational methods. To predict the solubility and viscosity of NADES, data-driven models were developed by combining group contribution and machine learning methods, based on the accumulated experimental knowledge on NADES found in the literature. Finally, the composed relationships and prediction models were applied to the practical example of deacetylation of mannosylerythritol lipids (MELs). The experimental findings show that the chosen NADES system has a significant effect on both the apparent initial activity and the final conversion. However, in the simulations, the enzyme retains its original structure; moreover, NADES has an additional stabilizing effect on the enzyme. In addition, changes in the molar ratio of the compounds in NADES do not show a significant effect on the stability of the enzyme. These results indicate that the main effect of NADES on the reaction is mainly related to the substrate-solvent interactions (solvation energy) and the viscosity of the system. On the other hand, the experimental results only confirmed the significance of solvation, viscosity did not show a clear correlation with the studied reaction parameters. The machine learning models built for solubility and viscosity gave quantitative predictions of these properties. The accumulated knowledge was used to optimize the yield in the deacetylation reaction of MELs. The combination of these methods provides fundamental knowledge about the effect of NADES on biocatalysis, but the results are also applicable to other uses of NADES.

Abstract: Natural deep eutectic solvents (NADES) are mixtures of naturally derived compounds with a significantly decreased melting point due to the specific interactions among the constituents. NADES have benign properties (low volatility, flammability, toxicity, cost) and tailorable physicochemical properties (by altering the type and molar ratio of constituents), hence they are often considered as a green alternative to common organic solvents. Modeling the relation between their composition and properties is crucial though, both for understanding and predicting their behavior. Several efforts were done to this end, yet this review aims at structuring the present knowledge as an outline for future research. First, we reviewed the key properties of NADES and relate them to their structure based on the available experimental data. Second, we reviewed available modeling methods applicable to NADES. At the molecular level, density functional theory and molecular dynamics allow interpreting density differences and vibrational spectra, and computation of interaction energies. Additionally, properties at the level of the bulk media can be explained and predicted by semi-empirical methods based on ab initio methods (COSMO-RS) and equation of state models (PC-SAFT). Finally, methods based on large datasets are discussed; models based on group contribution methods and machine learning. A combination of bulk media and dataset modeling allows qualitative prediction and interpretation of phase equilibria properties on the one hand, and quantitative prediction of melting point, density, viscosity, surface tension and refractive indices on the other hand. In our view, multiscale modeling, combining the molecular and macroscale methods, will strongly enhance the predictability of NADES properties and their interaction with solutes, yielding truly tailorable solvents to accommodate (bio)chemical reactions.

Abstract: Nanosecond pulsed discharge plasma shows a high degree of non-equilibrium, and exhibits relatively high conversions in the dry reforming of methane. To further improve the application, a good insight of the underlying mechanisms is desired. We developed a chemical kinetics model to explore the underlying plasma chemistry in nanosecond pulsed discharge. We compared the calculated conversions and product selectivities with experimental results, and found reasonable agreement in a wide range of specific energy input. Hence, the chemical kinetics model is able to provide insight in the underlying plasma chemistry. The modeling results predict that the most important dissociation reaction of CO₂and CH₄is electron impact dissociation. C₂H₂is the most abundant hydrocarbon product, and it is mainly formed upon reaction of two CH₂radicals. Furthermore, the vibrational excitation levels of CO₂contribute for 85% to the total dissociation of CO₂.

Abstract: NADPH oxidases 1 (NOX1) derived reactive oxygen species (ROS) play an important role in the progression of cancer through signaling pathways. Therefore, in this paper, we demonstrate the effect of cold atmospheric plasma (CAP) on the structural changes of Noxa1 SH3 protein, one of the regulatory subunits of NOX1. For this purpose, firstly we purified the Noxa1 SH3 protein and analyzed the structure using X-ray crystallography, and subsequently, we treated the protein with two types of CAP reactors such as pulsed dielectric barrier discharge (DBD) and Soft Jet for different time intervals. The structural deformation of Noxa1 SH3 protein was analyzed by various experimental methods (circular dichroism, fluorescence, and NMR spectroscopy) and by MD simulations. Additionally, we demonstrate the effect of CAP (DBD and Soft Jet) on the viability and expression of NOX1 in A375 cancer cells. Our results are useful to understand the structural modification/oxidation occur in protein due to reactive oxygen and nitrogen (RONS) species generated by CAP.

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Abstract: Multiple myeloma (MM) is a hematological malignancy characterized by plasma cells’ uncontrolled growth. The major barrier in treating MM is the occurrence of primary and acquired therapy resistance to anticancer drugs. Often, this therapy resistance is associated with constitutive hyperactivation of tyrosine kinase signaling. Novel covalent kinase inhibitors, such as the clinically approved BTK inhibitor ibrutinib (IBR) and the preclinical phytochemical withaferin A (WA), have, therefore, gained pharmaceutical interest. Remarkably, WA is more effective than IBR in killing BTK-overexpressing glucocorticoid (GC)-resistant MM1R cells. To further characterize the kinase inhibitor profiles of WA and IBR in GC-resistant MM cells, we applied phosphopeptidome- and transcriptome-specific tyrosine kinome profiling. In contrast to IBR, WA was found to reverse BTK overexpression in GC-resistant MM1R cells. Furthermore, WA-induced cell death involves covalent cysteine targeting of Hinge-6 domain type tyrosine kinases of the kinase cysteinome classification, including inhibition of the hyperactivated BTK. Covalent interaction between WA and BTK could further be confirmed by biotin-based affinity purification and confocal microscopy. Similarly, molecular modeling suggests WA preferably targets conserved cysteines in the Hinge-6 region of the kinase cysteinome classification, favoring inhibition of multiple B-cell receptors (BCR) family kinases. Altogether, we show that WA’s promiscuous inhibition of multiple BTK family tyrosine kinases represents a highly effective strategy to overcome GC-therapy resistance in MM.

Abstract: Monte Carlo (MC) methods have a long-standing history as partners of molecular dynamics (MD) to simulate the evolution of materials at the atomic scale. Among these techniques, the uniform-acceptance force-bias Monte Carlo (UFMC) method [ G. Dereli Mol. Simul. 8 351 (1992)] has recently attracted attention [ M. Timonova et al. Phys. Rev. B 81 144107 (2010)] thanks to its apparent capacity of being able to simulate physical processes in a reduced number of iterations compared to classical MD methods. The origin of this efficiency remains, however, unclear. In this work we derive a UFMC method starting from basic thermodynamic principles, which leads to an intuitive and unambiguous formalism. The approach includes a statistically relevant time step per Monte Carlo iteration, showing a significant speed-up compared to MD simulations. This time-stamped force-bias Monte Carlo (tfMC) formalism is tested on both simple one-dimensional and three-dimensional systems. Both test-cases give excellent results in agreement with analytical solutions and literature reports. The inclusion of a time scale, the simplicity of the method, and the enhancement of the time step compared to classical MD methods make this method very appealing for studying the dynamics of many-particle systems.

Abstract: Molecular dynamics simulations were used to investigate the size-dependent melting mechanism of nickel nanoclusters of various sizes. The melting process was monitored by the caloric curve, the overall cluster Lindemann index, and the atomic Lindemann index. Size-dependent melting temperatures were determined, and the correct linear dependence on inverse diameter was recovered. We found that the melting mechanism gradually changes from dynamic coexistence melting to surface melting with increasing cluster size. These findings are of importance in better understanding carbon nanotube growth by catalytic chemical vapor deposition as the phase state of the catalyst nanoparticle codetermines the growth mechanism.

Abstract: Molecular dynamics simulations using improved TersoffBrenner potential parameters were performed to investigate Cl+ etching of a {2×1} reconstructed Si(100) surface. Steady-state Si etching accompanying the Cl coverage of the surface is observed. Furthermore, a steady-state chlorinated reaction layer is formed. The thickness of this reaction layer is found to increase with increasing energy. The stoichiometry of SiClx species in the reaction layer is found to be SiCl:SiCl2:SiCl3 = 1.0:0.14:0.008 at 50 eV. These results are in excellent agreement with available experimental data. While elemental Si products are created by physical sputtering, most SiClx (0<x<4) etch products are produced by chemical-enhanced physical sputtering.

Abstract: Molecular dynamics simulations are used to investigate the effect of an AFM tip when indenting graphene nano bubbles filled by a noble gas (i.e. He, Ne and Ar) up to the breaking point. The failure points resemble those of viral shells as described by the Foppl-von Karman (FvK) dimensionless number defined in the context of elasticity theory of thin shells. At room temperature, He gas inside the bubbles is found to be in the liquid state while Ne and Ar atoms are in the solid state although the pressure inside the nano bubble is below the melting pressure of the bulk. The trapped gases are under higher hydrostatic pressure at low temperatures than at room temperature.

Abstract: Molecular dynamics simulations are carried out for describing growth of Pd and PdO nanoclusters using the ReaxFF force field. The resulting nanocluster structures are successfully compared to those of nanoclusters experimentally grown in a gas aggregation source. The PdO structure is quasi-crystalline as revealed by high resolution transmission microscope analysis for experimental PdO nanoclusters. The role of the nanocluster temperature in the molecular dynamics simulated growth is highlighted.

Abstract: Molecular dynamics (MD) simulations were performed to provide atomic scale insight in the initial interaction between hydroxyl radicals (OH) and peptide systems in solution. These OH radicals are representative reactive oxygen species produced by cold atmospheric plasmas. The use of plasma for biomedical applications is gaining increasing interest, but the fundamental mechanisms behind the plasma modifications still remain largely elusive. This study helps to gain more insight in the underlying mechanisms of plasma medicine but is also more generally applicable to peptide oxidation, of interest for other applications. Combining both reactive and nonreactive MD simulations, we are able to elucidate the reactivity of the amino acids inside the peptide systems and their effect on their structure up to 1 μs. Additionally, experiments were performed, treating the simulated peptides with a plasma jet. The computational results presented here correlate well with the obtained experimental data and highlight the importance of the chemical environment for the reactivity of the individual amino acids, so that specific amino acids are attacked in higher numbers than expected. Furthermore, the long time scale simulations suggest that a single oxidation has an effect on the 3D conformation due to an increase in hydrophilicity and intra- and intermolecular interactions.

Abstract: Modeling the oxidation process of silicon nanowires through reactive force field based molecular dynamics simulations suggests that the formation of Si epoxide defects occurs both at the Si/SiOx interface and at the nanowire surface, whereas for flat surfaces, this defect is experimentally observed to occur only at the interface as a result of stress. In this paper, we argue that the increasing curvature stabilizes the defect at the nanowire surface, as suggested by our density functional theory calculations. The latter can have important consequences for the opto-electronic properties of thin silicon nanowires, since the epoxide induces an electronic state within the band gap. Removing the epoxide defect by hydrogenation is expected to be possible but becomes increasingly difficult with a reduction of the diameter of the nanowires.

Abstract: Microwave plasmas are one of the most promising techniques for CO2 conversion into value-added chemicals and fuels since they are very energy efficient. Nevertheless, experiments show that this high energy efficiency is only reached at low pressures and significantly drops toward atmospheric pressure, which is a clear limitation for industrial applications. In this paper, we use a zerodimensional reaction kinetics model to simulate a CO2 microwave plasma in a pressure range from 50 mbar to 1 bar, in order to evaluate the reasons for this decrease in energy efficiency at atmospheric pressure. The code includes a detailed description of the vibrational kinetics of CO2, CO, and O2 as well as the energy exchanges between them because the vibrational kinetics is known to be crucial for energy efficient CO2 splitting. First, we use a self-consistent gas temperature calculation in order to assess the key performance indicators for CO2 splitting, i.e., the CO2 conversion and corresponding energy efficiency. Our results indicate that lower pressures and higher power densities lead to more vibrational excitation, which is beneficial for the conversion. We also demonstrate the key role of the gas temperature. The model predicts the highest conversion and energy efficiencies at pressures around 300 mbar, which is in agreement with experiments from the literature. We also show the beneficial aspect of fast gas cooling in the afterglow at high pressure. In a second step, we study in more detail the effects of pressure, gas temperature, and power density on the vibrational distribution function and on the dissociation and recombination mechanisms of CO2, which define the CO2 splitting efficiency. This study allows us to identify the limiting factors of CO2 conversion and to propose potential solutions to improve the process.

Abstract: Microdischarges are an important type of plasma discharge that possess several unique characteristics, such as the presence of a stable glow discharge, high plasma density and intense excimer radiation, leading to several potential applications. The intense and controllable gas heating within the extremely small dimensions of microdischarges has been exploited in microthruster technologies by incorporating a micro-nozzle to generate the thrust. This kind of microthruster has a significantly improved specific impulse performance compared to conventional cold gas thrusters, and can meet the requirements arising from the emerging development and application of micro-spacecraft. In this paper, we performed a self-consistent 2D particle-in-cell simulation, with a Monte Carlo collision model, of a microdischarge operating in a prototype micro-plasma thruster with a hollow cylinder geometry and a divergent micro-nozzle. The model takes into account the thermionic electron emission including the Schottky effect, the secondary electron emission due to cathode bombardment by the plasma ions, several different collision processes, and a non-uniform argon background gas density in the cathode-anode gap. Results in the high-pressure (several hundreds of Torr), high-current (mA) operating regime showing the behavior of the plasma density, potential distribution, and energy flux towards the hollow cathode and anode are presented and discussed. In addition, the results of simulations showing the effect of different argon gas pressures, cathode material work function and discharge voltage on the operation of the microdischarge thruster are presented. Our calculated properties are compared with experimental data under similar conditions and qualitative and quantitative agreements are reached.