Magnetically recyclable silica-coated magnetite-molybdate nanocatalyst and its applications in N-formylation reactions under solvent-free conditions-

A novel magnetite silica-coated nanoparticle-supported molybdate nanocatalyst has been prepared successfully by a simple co-precipitation method. Prepared nanocatalyst has been characterized by different techniques like Fourier Transmission Infrared Spectroscopy (FT-IR), X-ray diffraction (XRD), Field Emission Scanning Electron Microscope (FE-SEM) and Energy Dispersive X-ray Spectroscopy (EDX). Further, the catalytic activity of the nanocatalyst was explored for N-formylation reactions under solvent-free conditions. Interestingly, the catalyst could be reused for 10 cycles and only 2 mol % of the catalyst was sufficient to catalyze the N-formylation reaction at 700C under solvent-free conditions.

Core-Shell Nano-Antenna Configurations for Array Formation with More Stability Having Conventional and Non-Conventional Directivity and Propagation Behavior

The enhancement of optical characteristics at optical frequencies deviates with the choice of the arrangement of core-shell nanoparticles and their environment. Likewise, the arrangements of core-shell nanoparticles in the air over a substrate or in liquid solution makes them unstable in the atmosphere. This article suggests designing a configuration of an active spherical coated nanoparticle antenna and its extended array in the presence of a passive dielectric, which is proposed to be extendable to construct larger arrays. The issue of instability in the core-shell nanoantenna array models is solved here by inserting the passive dielectric. In addition to this, the inclusion of a dielectric in the array model reports a different directivity behaviour than the conventional array models. We found at first that the combination model of the active coated nanoparticle and passive sphere at the resonant frequency can excite a stronger field with a rotated polarization direction and a propagation direction different from the incident plane-wave. Furthermore, the extended 2D array also rotates the polarization direction and propagation direction for the vertical incident plane-wave. The radiation beam operates strong multipoles in the 2D array plane at resonant frequency (behaving non-conventionally). Nevertheless, it forms a clear main beam in the incident direction when it deviates from the resonance frequency (behaving conventionally). The proposed array model may have possible applications in nano-amplifiers, nano-sensors and other integrated optics.

Lipid-Coated Nanosized Drug Delivery Systems For An Effective Cancer Therapy

: Currently, despite many active compounds have been introduced to the treatment, cancer remains one of the most vital causes of mortality and reduced quality of life. Conventional cancer treatments may have undesirable consequences due to the continuously differentiating, dynamic and heterogeneous nature of cancer. Recent advances in the field of cancer treatment have promoted the development of several novel nanoformulations. Among them, the lipid coated nanosized drug delivery systems have gained an increasing attention by the researchers in this field owing to the attractive properties such as high stability and biocompatibility, prolonged circulation time, high drug loading capacity and superior in vivo efficacy. They possess the advantages of both the liposomes and polymeric nanoparticles which makes them a chosen one in the field of drug delivery and targeting. Core-shell type lipid-coated nanoparticle systems, which provide the most prominent advantages of both liposomes such as biocompatibility and polymeric/inorganic nanoparticles such as mechanic properties, offer a new approach to cancer treatment. This review discusses design and production procedures used to prepare lipid-coated nanoparticle drug delivery systems, their advantages and multifunctional role in cancer therapy and diagnosis, as well as the applications they have been used in.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

We combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model able to predicting the best components for Nanoparticle Drug Delivery Systems (DDNS).