Mesoporous Trimetallic PtPdRu Spheres as Superior Electrocatalysts

Mesoporous Trimetallic PtPdRu Spheres with well‐defined spherical morphology and uniformly sized pores were synthesized in an aqueous solution using ascorbic acid as the reducing agent and triblock copolymer F127 as the pore directing agent. These mesoporous PtPdRu spheres exhibited enhanced electrocatalytic activity compared to commercial Pt black, resulting in a ～4.9 times improvement in mass activity for the methanol oxidation reaction. The excellent electrocatalytic activity and stability are due to the unique mesoporous architecture and electronic landscape between different elements.     

The 4‐(Dimethylamino) benzaldehyde‐4‐ethylthiosemicarbazone as Ionophore for Mercury(II) Solid State Selective Electrode

Hg(II) has formed a soluble complex with 4‐(dimethylamino) benzaldehyde‐4‐ethylthiosemicarbazone (DMABET) in methanol with a molar ratio of mercury(II):DMABET of 1 : 4. The formation constant (K_f) and Gibbs free energy (?G) of the complex showed that the formation of the complex was favorable. The DMABET was investigated as ionophore for Hg(II)‐ion selective electrode (ISE). At optimum pH 1–5 the proposed Hg(II)‐ISE showed an almost Nernstian slope at 27.8±1 mV, with linear regression coefficient, R²=0.995 and a detection limit of 5×10^?6 M. There was no serious interference from silver(I) with selectivity coefficient 5.69×10^?3. The electrode life span was more than 3 months. It has been applied for real water sample analysis and the results were in good agreement with the standard method.     

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.     

Thickness effect on the structural and optical constants of stibnite thin films prepared by sulfidation annealing of antimony films

This paper deals with some physical properties of antimony sulphide Sb₂S₃ thin films obtained by an annealing process in sulphur vapors at 300 °C of Sb thermal evaporated thin films deposited on glass substrate. The crystal structure and surface morphology were investigated by both XRD and AFM techniques. This structural study shows that Sb₂S₃ thin films were well crystallized in orthorhombic structure and some parameters such as the lattice parameter, crystallite size, microstrain and degree of preferred orientation have been reported and correlated with the effect of crystallite size. On the other hand, the refractive index and the extinction coefficient were discussed in terms of the Forouhi–Bloomer model. The optical band gap was found to range from 1.75 to 2.23 eV. Finally, the analysis of the optical parameters extracted from the Urbach–Martienssen and Forouhi–Bloomer models lead to some explanations of the correlations between the structural properties in terms of the crystallite size and optical ones.     

Large-scale molecular dynamics simulations of dense plasmas: The Cimarron Project

We describe the status of a new time-dependent simulation capability for dense plasmas. The backbone of this multi-institutional effort – the Cimarron Project – is the massively parallel molecular dynamics (MD) code “ddcMD,” developed at Lawrence Livermore National Laboratory. The project’s focus is material conditions such as exist in inertial confinement fusion experiments, and in many stellar interiors: high temperatures, high densities, significant electromagnetic fields, mixtures of high- and low-Z elements, and non-Maxwellian particle distributions. Of particular importance is our ability to incorporate into this classical MD code key atomic, radiative, and nuclear processes, so that their interacting effects under non-ideal plasma conditions can be investigated. This paper summarizes progress in computational methodology, discusses strengths and weaknesses of quantum statistical potentials as effective interactions for MD, explains the model used for quantum events possibly occurring in a collision, describes two new experimental efforts that play a central role in our validation work, highlights some significant results obtained to date, outlines concepts now being explored to deal more efficiently with the very disparate dynamical timescales that arise in fusion plasmas, and provides a careful comparison of quantum effects on electron trajectories predicted by more elaborate dynamical methods.     

Aromatic Rings as Molecular Determinants for the Molecular Recognition of Protein Kinase Inhibitors

Protein kinases are key enzymes in many signal transduction pathways, and play a crucial role in cellular proliferation, differentiation, and various cell regulatory processes. However, aberrant function of kinases has been associated with cancers and many other diseases. Consequently, competitive inhibition of the ATP binding site of protein kinases has emerged as an effective means of curing these diseases. Over the past three decades, thousands of protein kinase inhibitors (PKIs) with varying molecular frames have been developed. Large-scale data mining of the Protein Data Bank resulted in a database of 2139 non-redundant high-resolution X-ray crystal structures of PKIs bound to protein kinases. This provided us with a unique opportunity to study molecular determinants for the molecular recognition of PKIs. A chemoinformatic analysis of 2139 PKIs resulted in findings that PKIs are “flat” molecules with high aromatic ring counts and low fractions of sp³ carbon. All but one PKI possessed one or more aromatic rings. More importantly, it was found that the average weighted hydrogen bond count is inversely proportional to the number of aromatic rings. Based on this linear relationship, we put forward the exchange rule of hydrogen bonding interactions and non-bonded π-interactions. Specifically, a loss of binding affinity caused by a decrease in hydrogen bonding interactions is compensated by a gain in binding affinity acquired by an increase in aromatic ring-originated non-bonded interactions (i.e., π–π stacking interactions, CH–π interactions, cation–π interactions, etc.), and vice versa. The very existence of this inverse relationship strongly suggests that both hydrogen bonding and aromatic ring-originated non-bonded interactions are responsible for the molecular recognition of PKIs. As an illustration, two representative PKI–kinase complexes were employed to examine the relative importance of different modes of non-bonded interactions for the molecular recognition of PKIs. For this purpose, two FDA-approved PKI drugs, ibrutinib and lenvatinib, were chosen. The binding pockets of both PKIs were thoroughly examined to identify all non-bonded intermolecular interactions. Subsequently, the strengths of interaction energies between ibrutinib and its interacting residues in tyrosine kinase BTK were quantified by means of the double hybrid DFT method B2PLYP. The resulting energetics for the binding of ibrutinib in tyrosine kinase BTK showed that CH–π interactions and π–π stacking interactions between aromatic rings of the drug and hydrophobic residues in its binding pocket dominate the binding interactions. Thus, this work establishes that, in addition to hydrogen bonding, aromatic rings function as important molecular determinants for the molecular recognition of PKIs. In conclusion, our findings support the following pharmacophore model for ATP-competitive kinase inhibitors: a small molecule features a scaffold of one or more aromatic rings which is linked with one or more hydrophilic functional groups. The former has the structural role of acting as a scaffold and the functional role of participating in aromatic ring-originated non-bonded interactions with multiple hydrophobic regions in the ATP binding pocket of kinases. The latter ensure water solubility and form hydrogen bonds with the hinge region and other hydrophilic residues of the ATP binding pocket.