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The efficient production of syngas from a CH4+CO2 mixture in an atmospheric pulsed glow discharge, sustained by corona pre-ionization, has been investigated. The products were mainly syngas (CO, H2) and hydrocarbons up to C4, with acetylene having the highest selectivity. The energy efficiency was within 15–40% for different experimental conditions, which demonstrates a comprehensive improvement relative to the achievements of other types of non-equilibrium plasma. These values are, however, comparable with the efficiencies obtained by gliding arc plasmas but this plasma operates at near room temperature. Furthermore, it has been shown that the energy efficiency is increased by decreasing the effective residence time. The effects of molar ratio CH4 : CO2, voltage, repetition rate and gas flow rate on conversion, energy efficiencies and the selectivities have also been investigated. The higher efficiency obtained in this kind of plasma is discussed and attributed to the short pulse regime and electric field uniformity.

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Mathematical modeling of biofiltration systems improves our understanding and design of such complex systems. This study focused on the theoretical and technical aspects of the modeling of xylene biofiltration in the absence and presence of a nonionic surfactant. In this regard, a mathematical model was developed based on mass balance principles in gas and biofilm phases. The developed model was calibrated and validated using the experimental data obtained from a lab‐scale scoria‐compost biofilter, which operated for 151 days in the absence and presence of Tween‐20, a nonionic surfactant. First, the model was calibrated using the experimental data obtained at empty bed retention time (EBRT) of 90 s and then validated with the data obtained at two other EBRTs. The biofilter provided maximum elimination capacities (ECmax) of 97.5 and 93.6 g m−3 hr−1, respectively, in the absence and presence of the surfactant at EBRT of 90 s. The corresponding predicted ECmax values were 99.9 and 95.7 g m−3 hr−1, respectively. Both model output and experimental data revealed that the nonionic surfactant improved the performance of the biofilter at moderate inlet loading rates. Various statistical measures, including fractional bias, average absolute relative error, and coefficient of determination (R2), showed good agreement between experimental data and estimated model predictions. Sensitivity analysis of the model showed that the specific surface area and bioreactor length affected strongly the results of the model. In general, the results of this study would in turn form the design basis for engineering purposes.

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.

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