Modification of magnetic anisotropy in metallic glasses using high-energy ion beam irradiation

Heavy ion irradiation in the electronic stopping power region induces macroscopic dimensional change in metallic glasses and introduces magnetic anisotropy in some magnetic materials. The present work is on the irradiation study of ferromagnetic metallic glasses, where both dimensional change and modification of magnetic anisotropy are expected. Magnetic anisotropy was measured using Mössbauer spectroscopy of virgin and irradiated Fe₄₀Ni₄₀B₂₀ and Fe₄₀Ni₃₈Mo₄B₁₈ metallic glass ribbons. 90 MeV ¹²⁷I beam was used for the irradiations. Irradiation doses were 5×10¹³ and 7.5×10¹³ ions/cm². The relative intensity ratios D ₂₃ of the second and third lines of the Mössbauer spectra were measured to determine the magnetic anisotropy. The virgin samples of both the materials display in-plane magnetic anisotropy, i.e., the spins are oriented parallel to the ribbon plane. Irradiation is found to cause reduction in magnetic anisotropy. Near-complete randomization of magnetic moments is observed at high irradiation doses. Correlation is found between the residual stresses introduced by ion irradiation and the change in magnetic anisotropy.     

In-situ green synthesis of highly active GSH-capped Pt–Au–Ag-hybrid nanoclusters

Recently much attention has been paid to the application of metal hybrid nanoparticles in industrial catalytic fields because of their super-efficient catalytic activity and attractive properties. We explored a novel strategy to prepare GSH-capped Pt–Au–Ag-hybrid nanoclusters through the synergistic effect between ascorbic acid(VC) and glutathione(GSH) with chloroplatinic acid, chloroauric acid, and silver nitrate as precursors. The potential utilization of as-prepared GSH-capped Pt–Au–Aghybrid nanoclusters for catalytic applications has been evaluated through the reduction of 4-nitrophenol(4-NP) with NaBH4; we obtained the kinetic data by monitoring with UV-Vis spectroscopy. Our results illustrate that GSH-capped Pt–Au– Ag-hybrid nanoclusters could facilitate the process of reduction of 4-NP in a way that is unprecedented. This approach may offer a novel, non-cytotoxicity, efficient catalyst for industry.     

Synthesis of pyrano[2,3-c]pyrazoles: A review

One-pot synthesis of pyrano[2,3-c]pyrazole derivatives via two or multicomponent condensation of β-ketoesters, hydrazine monohydrate/phenyl hydrazine, malononitrile, and substituted benzaldehydes is of great interest not only in synthetic chemistry but also in medicinal chemistry because of the tremendous biological activities, such as anticancer, anti-inflammatory, analgesic, antimicrobial, and enzyme inhibitory activities exhibited by pyranopyrazoles. This review provides extensive knowledge on novel synthetic methodologies of biologically active non-spiro/spiro-pyrano[2,3-c]pyrazole derivatives published recently.     

Ferroptosis: A New Road towards Cancer Management

Ferroptosis is a recently described programmed cell death mechanism that is characterized by the buildup of iron (Fe)-dependent lipid peroxides in cells and is morphologically, biochemically, and genetically distinct from other forms of cell death, having emerged to play an important role in cancer biology. Ferroptosis has significant importance during cancer treatment because of the combination of factors, including suppression of the glutathione peroxidase 4 (Gpx4), cysteine deficiency, and arachidonoyl (AA) peroxidation, which cause cells to undergo ferroptosis. However, the physiological significance of ferroptosis throughout development is still not fully understood. This current review is focused on the factors and molecular mechanisms with the diagrammatic illustrations of ferroptosis that have a role in the initiation and sensitivity of ferroptosis in various malignancies. This knowledge will open a new road for research in oncology and cancer management.     

Parallel analysis of v-Src mutant protein function using reverse transfection cell arrays

The conversion of the genomic information produced by the recent sequencing projects into a comprehensive understanding of the human proteome has yet to occur. A new technology that represents a potential bridge between genomics and proteomics is reverse transfection. Reverse transfection cell microarrays are produced by overlaying cDNA arrays with mammalian cells, generating localized clusters of transfected cells with each cluster overexpressing a unique protein. This miniaturized cell-based microarray format affords parallel functional analysis of thousands of cDNA constructs in a high throughput format. In this report we document the development of a co-transfection methodology for reverse transfection applications. The demonstrated high co-transfection efficiency with a "marker" plasmid encoding for GFP enables the identification of transfected cells and eliminates the need for epitope-tagged constructs in cell-based high throughput screening applications using reverse transfection. This co-transfection method was used to study in parallel the structure/function of multiple versions of the v-Src protein using automated fluorescence microscopy. The wild-type v-Src protein and four mutants having insertions or deletions in the SH2 or SH3 domains displayed high levels of tyrosine kinase activity in HEK293T cells. Three other mutated v-Src proteins, including a kinase-dead version, were shown to be defective for tyrosine kinase activity. This reverse co-transfection approach is applicable for high throughput screening of both cDNA libraries and positional scanning recombinant protein libraries.     

Facile one-pot synthesis,butyrylcholinesterase and α-glucosidase inhibitory activities,structure–activity relationship,molecular docking and DNA–drug binding analysis of Meldrum’s acid derivatives

Research on Chemical Intermediates - Meldrum’s acid derivatives were facile synthesized by one-pot condensation process and characterized by NMR (1H, 13C, DEPT-90 and DEPT-135) and EI-MS. The...     

Falkner–Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall

In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.     

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.     

Axial-flexural coupled vibration and buckling of composite beams using sinusoidal shear deformation theory

A finite element model based on sinusoidal shear deformation theory is developed to study vibration and buckling analysis of composite beams with arbitrary lay-ups. This theory satisfies the zero traction boundary conditions on the top and bottom surfaces of beam without using shear correction factors. Besides, it has strong similarity with Euler–Bernoulli beam theory in some aspects such as governing equations, boundary conditions, and stress resultant expressions. By using Hamilton’s principle, governing equations of motion are derived. A displacement-based one-dimensional finite element model is developed to solve the problem. Numerical results for cross-ply and angle-ply composite beams are obtained as special cases and are compared with other solutions available in the literature. A variety of parametric studies are conducted to demonstrate the effect of fiber orientation and modulus ratio on the natural frequencies, critical buckling loads, and load-frequency curves as well as corresponding mode shapes of composite beams.     

Four-dimensional parameter estimation of plane waves using swarming intelligence

This paper proposes an efficient approach for four-dimensional(4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional(2D) direction of arrival,namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose,a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte–Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise.