Orientation dependence in high harmonics of ZnO with polarization corrections to counteract the birefringent effect

We investigate the influence of the birefringence on the high-order harmonics in an a-cut Zn O crystal with midinfrared laser pulses. The high harmonics exhibit strong dependence on the alignment of the crystal with respect to the laser polarization. We introduce the Jones calculus to counteract the birefringent effect and obtain the harmonics with polarization corrections in Zn O. We show that the birefringent effect plays an important role in the orientation dependence of HHG.     

Nanoporous NiO@SiO2 photo-catalyst prepared by ion-exchange method for fast elimination of reactive dyes from wastewater

Photo-catalytic elimination of organic contaminants plays a significant role in wastewater treatment. Developing a highly efficient photo-catalyst is one of the leading research topic. Herein, we reported the fabrication of a novel nanoporous NiO@SiO₂ photo-catalyst by a simple ion-exchange method to eliminate the reactive dyes. The synthesized NiO@SiO₂ catalyst exhibited fast photo-degradation and excellent adsorption capability and could efficiently remove Red FN-3GL dye from wastewater, due to a high loading of NiO and a large specific surface area, abundant electron-withdrawing groups, as well as narrow bandgap energy. In addition, the NiO@SiO₂ photo-catalyst also displayed a high capability to remove reactive dyes over a wide range of pH values (pH 3–9). The prominent adsorption and photo-degradation of dyes were strongly dependent on the surface charge of the catalyst and the generation of hydroxyl radicals (OH^?) by the catalyst, respectively. Furthermore, the NiO@SiO₂ photo-catalyst also exhibited excellent recyclability, thus demonstrating the feasibility of practical applications in industries. The strategy of covering the metal oxide to nanoporous silica is a promising method for developing active photo-catalysts and applying them in the wastewater treatments.     

Bicomponent Cocatalyst Decoration on Flux-assisted CaTaO2N Single Crystals for Photocatalytic CO2 Reduction under Visible Light

Cubic-like CaTaO₂N photocatalysts with high crystallinity and uniform particle size were successfully prepared by the flux-assisted nitridation method. The growth of CaTaO₂N single crystals under different synthesis conditions was systematically investigated to understand the effects of the crystallinity and optical property on photocatalytic performance of CO₂ reduction. Moreover, the modification of CaTaO₂N single crystals with core-shell Ni−Ag bicomponent cocatalyst by two-step decoration process gave a 2.4 times higher amount of CO evolution than the deposition of sole Ag cocatalyst, because of the synergistic effects of bicomponent cocatalyst on the interfacial electron transfer and surface catalytic process. This study provides a valuable way to construct high-crystalline photocatalysts with effective bicomponent cocatalyst for visible-light-driven CO₂ reduction with H₂O.     

Electric potential and energy band in ZnO nanofiber tuned by local mechanical loading

Recent success in strain engineering has triggered tremendous interest in its study and potential applications in nanodevice design. In this paper, we establish a coupled piezoelectric/semiconducting model for a wurtzite structure ZnO nanofiber under the local mechanical loading. The energy band structure tuned by the local mechanical loading and local length is calculated via an eight-band k·p method, which includes the coupling of valance and conduction bands. Poisson's effect on the distribution of electric potential inversely depends on the local mechanical loading. Numerical results reveal that both the applied local mechanical loading and the local length exhibit obvious tuning effects on the electric potential and energy band. The band gap at band edges varies linearly with the applied loading. Changing the local length shifts the energy band which is far away from the band edges. This study will be useful in the electronic and optical enhancement of semiconductor devices.     

Unconditionally optimal convergence of an energy-conserving and linearly implicit scheme for nonlinear wave equations

In this paper, we present and analyze an energy-conserving and linearly implicit scheme for solving the nonlinear wave equations. Optimal error estimates in time and superconvergent error estimates in space are established without certain time-step restrictions. The key is to estimate directly the solution bounds in the H²-norm for both the nonlinear wave equation and the corresponding fully discrete scheme, while the previous investigations rely on the temporal-spatial error splitting approach. Numerical examples are presented to confirm energy-conserving properties, unconditional convergence and optimal error estimates, respectively, of the proposed fully discrete schemes.

     

Yield enhancement of elliptical high harmonics driven by bicircular laser pulses

We theoretically investigate the yield enhancement of elliptical high harmonics in the interaction of molecules with bicircular laser pulses by solving the time-dependent Schrödinger equation. It is shown that by adjusting the relative intensity ratio of the two bicircular field components in specific ranges the yield of the molecular high harmonics for the plateau and cutoff regions can be respectively enhanced. To analyze this enhancement phenomenon, we calculate the weights of the electron classical trajectories. Additionally, we also study the ellipticity distribution of harmonics for different intensity ratios. We find that these enhanced harmonics are elliptically polarized, which we mainly attribute to the recombination dipole moment of the major weighted trajectories. These enhanced elliptical extreme ultraviolet and soft x-ray radiations may serve as essential tools for exploring the ultrafast dynamics in magnetic materials and chiral media.     

Synthesis and properties of novel N,C,N terdentate skeleton based on 1,3-di(pyridin-2-yl)benzene moiety-new tricks for old dogs

Utilization of intermolecular Friedel-Crafts and intramolecular condensation reaction,novel 1,3-di-(pyridine-2-yl)benzene(N,C,N terdentate) skeleton with electro-withdrawing group in 6' position of pyridyl and a cyclization between 6' position of pyridyl and 6 position of benzyl ring were firstly designed and synthesized.The structures of these novel N,C,N terdentate were confirmed by NMR,MS and X-ray single crystalanalyses.The photophysical properties of these compounds were briefly explored.     

Graphene and graphene like 2D graphitic carbon nitride: Electrochemical detection of food colorants and toxic substances in environment

Excessive consumption of substances such as food colorants, exposure to doses of metal ions, antibiotic residues and pesticides residues above maximum tolerance limit have a detrimental effect on human health. Hence in detecting these harmful substances, the development of sensitive, selective and convenient analytical tools is an essential step. Graphene and graphene like 2D graphitic carbon nitride have shown great promise in the development of electrochemical sensors for determining the levels of these substances in different samples. In this paper, graphene and graphene like 2D graphitic carbon nitride applications on the determination of various food colorants in foods and drinks such as azo dyes (tartrazine, allura red, amaranth, carmine and sunset yellow); metal ions contaminants, antibiotic and pesticide residues in the environment are reviewed.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.