Combined Experimental and Theoretical Insights into the Corrosion Inhibition Activity on Carbon Steel Iron of Phosphonic Acids

The inhibition effect of N,N′-phosphonomethylglycine (PMG) and vinyl phosphonic acid (VPA) on the 3% NaCl acidic solution corrosion of carbon steel iron was studied at different immersion times by potentiodynamic polarization, electrochemical impedance spectroscopy, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, and computational methods. It is found from the polarization studies that PMG and VPA behave as mixed-type inhibitors in NaCl. Values of charge transfer resistance (R_ct) and double layer capacitance (C_dl) in the absence and presence of inhibitors are determined. The PMG and VPA inhibitors were capable of inhibiting the corrosion process up to ≈91% and ≈85%, respectively. In the presence of PMG, the synergic effect of chlorine ions was observed. Density functional theory (DFT) was engaged to establish the adsorption site of PMG, VPA, and their deprotonated states. For studied compounds, the resulted values of E_LUMO, E_HOMO, energy gap (∆E), dipole moment (μ), electronic hardness (η), global softness (σ), electrophilic index (ω), and the electronic potential map are in concordance with the experimental data results regarding their corrosion inhibition behavior and adsorption on the metal surface.     

Identification of Potential SARS-CoV-2 Main Protease and Spike Protein Inhibitors from the Genus Aloe: An In Silico Study for Drug Development

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) disease is a global rapidly spreading virus showing very high rates of complications and mortality. Till now, there is no effective specific treatment for the disease. Aloe is a rich source of isolated phytoconstituents that have an enormous range of biological activities. Since there are no available experimental techniques to examine these compounds for antiviral activity against SARS-CoV-2, we employed an in silico approach involving molecular docking, dynamics simulation, and binding free energy calculation using SARS-CoV-2 essential proteins as main protease and spike protein to identify lead compounds from Aloe that may help in novel drug discovery. Results retrieved from docking and molecular dynamics simulation suggested a number of promising inhibitors from Aloe. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) calculations indicated that compounds 132, 134, and 159 were the best scoring compounds against main protease, while compounds 115, 120, and 131 were the best scoring ones against spike glycoprotein. Compounds 120 and 131 were able to achieve significant stability and binding free energies during molecular dynamics simulation. In addition, the highest scoring compounds were investigated for their pharmacokinetic properties and drug-likeness. The Aloe compounds are promising active phytoconstituents for drug development for SARS-CoV-2.     

Al2O3/PDMS复合材料热传导的分子动力学模拟

采用分子动力学模拟方法研究了Al₂O₃/聚甲基二硅氧烷(PDMS)复合材料在300 K时的传热行为, 通过分析热传导、温度梯度以及导热增强等理论数据, 讨论了不同半径以及不同浓度的Al₂O₃填料粒子对PDMS传热的影响. 结果表明随着体积分数的增大, Al₂O₃/PDMS复合材料的热传导先减小后增加. 并且当Al₂O₃填充粒子的半径为5 nm时, Al₂O₃/PDMS复合材料的热传导达到最大值. 通过考察传热过程中的Al₂O₃填料和Al₂O₃/PDMS复合材料的温度和结构变化, 添加有小半径Al₂O₃填料的Al₂O₃/PDMS复合材料在传热的过程中主要以声子的形式进行传热, 此时决定热导率的主要因素有热容c、声子的平均速度v和声子的平均自由程l. 同时, 当体积分数为15%时, 传导链形成, 热量可以顺利通过Al₂O₃填料形成的传导链传播, 有助于热传导的增加.     

How to Predict Excited State Geometry by Using Empirical Parameters Obtained from Franck-Condon Analysis of Optical Spectrum

Excited state geometries of molecules can be calculated with highly reliable wavefunction schemes. Most of such schemes, however, are applicable to small molecules and can hardly be viewed as error-free for excited state geometries. In this study, a theoretical approach is presented in which the excited state geometries of molecules can be predicted by using vibrationally resolved experimental absorption spectrum in combination with the theoretical modelling of vibrational pattern based on Franck-Condon approximation. Huang-Rhys factors have been empirically determined and used as input for revealing the structural changes occurring between the ground and the excited state geometries upon photoexcitation. Naphthalene molecule has been chosen as a test case to show the robustness of the proposed theoretical approach. Predicted 1B_2u excited state geometry of the naphthalene has similar but slightly different bond length alternation pattern when compared with the geometries calculated with CIS, B3LYP, and CC2 methods. Excited state geometries of perylene and pyrene molecules are also determined with the presented theoretical approach. This powerful method can be applied to other molecules and specifically to relatively large molecules rather easily as long as vibrationally resolved experimental spectra are available to use.     

Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis

Type 2 diabetes mellitus (T2DM) is one of the most widely prevalent metabolic disorders with no cure to date thus remains the most challenging task in the current drug discovery. Therefore, the only strategy to control diabetes prevalence is to develop novel efficacious therapeutics. Dipeptidyl Peptidase 4 (DPP-4) inhibitors are currently used as anti-diabetic drugs for the inhibition of incretins. This study aims to construct the chemical feature based on pharmacophore models for dipeptidyl peptidase IV. The structure-based pharmacophore modeling has been employed to evaluate new inhibitors of DPP-4. A four-featured pharmacophore model was developed from crystal structure of DPP-4 enzyme with 4-(2-aminoethyl) benzenesulfonyl fluoride in its active site via pharmacophore constructing tool of Molecular Operating Environment (MOE) consisting F1 Hyd (hydrophobic region), F2 Hyd|Cat|Don (hydrophobic cationic and donor region), F3 Acc (acceptor region) and F4 Hyd (hydrophobic region). The generated pharmacophore model was used for virtual screening of in-house compound library (the available compounds which were used for initial screening to get the few compounds for the current studies). The resultant selected compounds, after virtual screening were further validated using in vitro assay. Furthermore, structure-activity relationship was carried out for the compounds possessing significant inhibition potential after docking studies. The binding free energy of analogs was evaluated via molecular mechanics generalized Born surface area (MM-GBSA) and Poisson-Boltzmann surface area (MM-PBSA) methods using AMBER 16 as a molecular dynamics (MD) simulation package. Based on potential findings, we report that selected candidates are more likely to be used as DPP-4 inhibitors or as starting leads for the development of novel and potent DPP-4 inhibitors.     

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling,molecular docking,and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.     

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.     

Numerical Investigation of Graphene as a Back Surface Field Layer on the Performance of Cadmium Telluride Solar Cell

This paper numerically explores the possibility of ultrathin layering and high efficiency of graphene as a back surface field (BSF) based on a CdTe solar cell by Personal computer one-dimensional (PC1D) simulation. CdTe solar cells have been characterized and studied by varying the carrier lifetime, doping concentration, thickness, and bandgap of the graphene layer. With simulation results, the highest short-circuit current (I_sc = 2.09 A), power conversion efficiency (η = 15%), and quantum efficiency (QE~85%) were achieved at a carrier lifetime of 1 × 10³ μs and a doping concentration of 1 × 10¹⁷ cm⁻³ of graphene as a BSF layer-based CdTe solar cell. The thickness of the graphene BSF layer (1 μm) was proven the ultrathin, optimal, and obtainable for the fabrication of high-performance CdTe solar cells, confirming the suitability of graphene material as a BSF. This simulation confirmed that a CdTe solar cell with the proposed graphene as the BSF layer might be highly efficient with optimized parameters for fabrication.     

Influence of Substitutional Defects in ZIF-8 Membranes on Reverse Osmosis Desalination: A Molecular Dynamics Study

In this study, molecular dynamics simulation is used to investigate the effects of water-based substitutional defects in zeolitic imidazolate frameworks (ZIF)-8 membranes on their reverse osmosis (RO) desalination performance. ZIF-8 unit cells containing up to three defect sites are used to construct the membranes. These substitutional defects can either be Zn defects or linker defects. The RO desalination performance of the membranes is assessed in terms of the water flux and ion rejection rate. The effects of defects on the interactions between the ZIF-8 membranes and NaCl are investigated and explained with respect to the radial distribution function (RDF) and ion density distribution. The results show that ion adsorption on the membranes occurs at either the nitrogen atoms or the defect sites. Complete NaCl rejection can be achieved by introducing defects to change the size of the pores. It has also been discovered that the presence of linker defects increases membrane hydrophilicity. Overall, molecular dynamics simulations have been used in this study to show that water-based substitutional defects in a ZIF-8 structure reduce the water flux and influence its hydrophilicity and ion adsorption performance, which is useful in predicting the type and number of defect sites per unit cell required for RO applications. Of the seven ZIF-8 structures tested, pristine ZIF-8 exhibits the best RO desalination performance.     

Monte Carlo simulation of full energy spectrum of electrons emitted from silicon in Auger electron spectroscopy

A Monte Carlo simulation including surface excitation, Auger electron‐ and secondary electron production has been performed to calculate the energy spectrum of electrons emitted from silicon in Auger electron spectroscopy (AES), covering the full energy range from the elastic peak down to the true‐secondary‐electron peak. The work aims to provide a more comprehensive understanding of the experimental AES spectrum by integrating the up‐to‐date knowledge of electron scattering and electronic excitation near the solid surface region. The Monte Carlo simulation model of beam–sample interaction includes the atomic ionization and relaxation for Auger electron production with Casnati's ionization cross section, surface plasmon excitation and bulk plasmon excitation as well as other bulk electronic excitation for inelastic scattering of electrons (including primary electrons, Auger electrons and secondary electrons) through a dielectric functional approach, cascade secondary electron production in electron inelastic scattering events, and electron elastic scattering with use of Mott's cross section. The simulated energy spectrum for Si sample describes very well the experimental AES EN(E) spectrum measured with a cylindrical mirror analyzer for primary energies ranging from 500 eV to 3000 eV. Surface excitation is found to affect strongly the loss peak shape and the intensities of the elastic peak and Auger peak, and weakly the low energy backscattering background, but it has less effect to high energy backscattering background and the Auger electron peak shape. Copyright © 2014 John Wiley & Sons, Ltd.