Investigation of MHD effect on nanofluid heat transfer in microchannels

Journal of Thermal Analysis and Calorimetry - An incompressible preconditioned lattice Boltzmann method (IPLBM) is proposed to investigate the fluid flow and heat transfer characteristics of...     

Terpolymerization of Triisopropylsilyl Acrylate,Methyl Methacrylate,and Butyl Acrylate: Reactivity Ratio Estimation

Ternary monomer reactivity ratios of triisopropylsilyl acrylate (SiA), methyl methacrylate (MMA), and n‐butyl acrylate (BA), as common monomers in self‐polishing coatings (SPCs) binders are obtained using experimental data collected from free radical bulk polymerization at 70 °C. Different terpolymerizations at low and medium‐high conversions are performed at optimized feed compositions. Estimations are made using the error‐in‐variables model (EVM) framework, applying the recast form of the Alfrey–Goldfinger (AG) model and a direct numerical integration (DNI) approach to the collected data. Estimations from individual low and medium‐high conversion data are compared to those found with the combined data (full conversion range data). The highest certainty in point estimates are obtained with analysis of the full conversion range data. Furthermore, the reactivity ratios determined from the combined data fall between those found with analysis of individual low and medium‐high conversion data, another corroboration of reliable data collection. Reactivity ratios determined from analysis of the combined data (r_SiA/MMA = 0.4185, r_MMA/SiA = 1.3754, r_SiA/BA = 0.8739, r_BA/SiA = 0.5736, r_BA/MMA = 0.3692, r_MMA/BA = 1.7919) are used in the recast AG model to predict cumulative terpolymer composition as a function of conversion. The experimental data and model prediction show satisfactory agreement.     

Synthesis and characterization of PEGylated iron and graphene oxide magnetic composite for curcumin delivery

Nowadays, nanostructures have been given significant attention in medical and biological fields. Among these nanostructures, graphene oxide (GO) has been widely used in drug delivery systems, because of its unique properties, and the ability to connect to other nanostructures such as magnetic nanoparticles (NPs) as well as polymers by its functional groups. In this research, first, GO was prepared by exfoliating graphite according to the modified Hummer’s method, and then the Fe₃O₄ NPs were synthesized by a simple co-precipitation method on GO nanosheets. In the next step, with the help of the ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide coupling reagents, the polyethylene glycol (PEG) polymer was bonded to the GO-Fe₃O₄ nanocomposite. Finally, anti-cancer drug, curcumin (Cur) was loaded onto the nanocomposite and the Cur loading ratio was measured at about 8%. The samples were evaluated using Fourier transform-infrared, differential scanning calorimtery, vibrating-sample magnetometry, atomic force microscopy and dynamic light scattering techniques. The results show that the prepared nanocomposite is an appropriate candidate for biomedical applications.     

Effect of the Solvent Polarity on the Optical Properties in the (OC)5Cr=(OEt)(Ph) Complex: A Quantum Chemical Study

Russian Journal of Physical Chemistry A - In this study, using the B3LYP* method, quantum chemical computations were applied for analyzing the effects of solvent on the electronic spectrum features...     

Zn and Zr co-doped M-type strontium hexaferrite: Synthesis,characterization and hyperthermia application

In the present study, hard ferromagnetic (M-type strontium hexaferrite) SrFe₁₂O₁₉ was co-doped by Zn and Zr for magnetic hyperthermia applications. As a result of the high concentration of single domain SrFe₁₂O₁₉ nanoparticles (suspended in the ferrofluid), they found a large hydrodynamic diameter, which caused a long-time Brownian relaxation under the AC magnetic field. On the other hand, increasing the Zn-Zr content (low concentration of SrFe₁₂O₁₉) led to a drop in anisotropy, which coincided with a short-time N´eel relaxation. All of the substituted samples with a multi-disperse state in ferrofluid exhibited an almost equal amount of the N´eel and Brownian effects. Consequently, the magnetic saturation (M_s) was considered as the dominant factor in the specific absorption rate (SAR) of the substituted samples. Transformation to the mono-disperse state was followed by the decrease of the Brownian relaxation time and hence the increase of the SAR. The interesting point in mono-disperse state was the heat generation of pure SrFe₁₂O₁₉ under the AC magnetic field as a result of the decrement of the Brownian relaxation time.     

Effective Mass of Radion via GW Mechanism with a Non-Minimally Coupled Bulk Scalar Field

In the Randall-Sundrum braneworld scenario via the Goldberger-Wise mechanism, the effective mass of 4D radion, where the bulk scalar field is coupled to 5D gravity, has been investigated in two different case of the self-coupling constants, infinite and finite cases. It is shown that in the finite case, the modulus parameter should be greater than the finite coupling constants.     

Dust acoustic solitary and shock waves in strongly coupled dusty plasmas with nonthermal ions

The Korteweg-de Vries-Burgers (KdV-Burgers) equation and modified Korteweg-de Vries-Burgers equation are derived in strongly coupled dusty plasmas containing nonthermal ions and Boltzmann distributed electrons. It is found that solitary waves and shock waves can be produced in this medium. The effects of important parameters such as ion nonthermal parameter, temperature, density and velocity on the properties of shock waves and solitary waves are discussed.     

Neural Estimator of Information for Time-Series Data with Dependency

Novel approaches to estimate information measures using neural networks are well-celebrated in recent years both in the information theory and machine learning communities. These neural-based estimators are shown to converge to the true values when estimating mutual information and conditional mutual information using independent samples. However, if the samples in the dataset are not independent, the consistency of these estimators requires further investigation. This is of particular interest for a more complex measure such as the directed information, which is pivotal in characterizing causality and is meaningful over time-dependent variables. The extension of the convergence proof for such cases is not trivial and demands further assumptions on the data. In this paper, we show that our neural estimator for conditional mutual information is consistent when the dataset is generated with samples of a stationary and ergodic source. In other words, we show that our information estimator using neural networks converges asymptotically to the true value with probability one. Besides universal functional approximation of neural networks, a core lemma to show the convergence is Birkhoff’s ergodic theorem. Additionally, we use the technique to estimate directed information and demonstrate the effectiveness of our approach in simulations.