The prediction of maximum temperature for single chips’ cooling using artificial neural networks

A CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R ² values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R ² of 0.99994.     

Synthesis of novel metal/bimetal nanoparticle-modified ZSM-5 zeolite nanocomposite catalysts and application on toluene methylation

Research on Chemical Intermediates - Synthesis of Ag, Ni, Cu metallic and Ag/Ni, Ag/Cu bimetallic nanoparticles (NPs) was carried out by using metal nitrate salts as precursors via an easy and...     

An experimental and computational approach to pH‐dependent self‐aggregation of poly(2‐isopropyl‐2‐oxazoline)

Besides temperature, self‐aggregation of poly(2‐isopropyl‐2‐oxazoline) (PIPOX) can also be triggered via pH in aqueous solution (25 °C, pH > 5). Lowest energy structures and interaction energies of PIPOX with H₃O⁺, OH^?, and H₂O were calculated by DFT methods showed that, in addition to their ability to protonate PIPOX, H₃O⁺ ions had strong interaction with both water and PIPOX in acidic conditions. H₃O⁺ ions acted as compatibilizer between PIPOX and water and increased the solubility of PIPOX. OH^? ions were found to have stronger interaction with water compared to PIPOX resulting in desorption of water molecules from PIPOX phase and decreased solubility, leading to enhanced hydrophobic interactions among isopropyl groups of PIPOX and formation of aggregates at high pH. Results concerning the effect of end‐groups on aggregate size were in good agreement with statistical mechanics calculations. Moreover, the effect of polymer concentration on the aggregate size was examined. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 210–221     

Development An Amperometric Microbial-enzyme Hybrid Cholesterol Biosensor Based On Ionic Liquid MWCNT Carbon Paste Electrode

In this study we report the development of an amperometric cholesterol biosensor based on cholesterol oxidase from Pseudomonas sp. and catalase immobilized in carbon paste electrode (CPE) modified with multiwall carbon nanotubes (MWCNT) and ionic liquid (IL). The working electrode (CPE/MWCNT-IL/Microorganism (MO)-Catalase) was characterized by impedance spectroscopy and cyclic voltammetry at different stages of its construction. This proposed cholesterol biosensor performed linear relationship in the range of 5–600 μM with a low detection limit of 1.52 μM. The biosensor showed good sensitivity and high selectivity and it was successfully applied for the measurement of cholesterol levels in lyophilized serum samples.     

Effects of structural isomerism on solution behaviour of solutes: Apparent molar volumes and isentropic compression of catechol,resorcinal, and hydroquinone in aqueous solution at T = (283.15, 293.15, 298.15, 303.15, and 313.15) K

Effects of structural isomerism on solution behaviour of dihydroxybenzenes were examined through the determination of volumetric properties such as apparent molar volumes, apparent molar isentropic compressions, and isobaric expansions. The isomers were 1,2-dihydroxybenzene (catechol), 1,3-dihydroxybenzene (resorcinol), and 1,4-dihydroxybenzene (hydroquinone). The volumetric properties were determined from accurate density and speed of sound measurements at T = (283.15, 293.15, 298.15, 303.15, and 313.15) K and at various concentrations. Values at infinite dilution of these parameters were obtained by suitable extrapolation procedures. The results are discussed in terms of hydrophobic, hydrogen bonding, and dipole–dipole interactions between the three isomers and water. Catechol was found to have the strongest hydrophilic and the weakest hydrophobic interactions with water among the three isomers.     

Label free capacitive immunosensor for detecting calpastatin — A meat tenderness biomarker

An immunological capacitive biosensor for calpastatin was developed, optimized and applied for the analysis of meat extract samples. Anti-calpastatin antibody was immobilized on a gold electrode modified with a self-assembled monolayer of mercaptoundecanoic acid and Protein A from Staphylococcus aureus, and the obtained immunosensor was inserted as the working electrode in an electrochemical cell of a flow injection system. The dynamic range of the sensor was 20 to 160 ng/mL calpastatin. The electrode could be regenerated and re-used for more than 7 days with minimal reduction in sensitivity. For the analysis of real samples, the target analyte was extracted from the Longissimus dorsi muscle from beef carcasses directly after slaughtering. The extract was analyzed both with the developed immunosensor and microtiter plate ELISA, and a good correlation was obtained. However the immunosensor offers advantages of speed, simplicity, sensitivity and possibility for miniaturization over conventional assays for calpastatin quantification.     

Effect of cationic surfactant adsorption on the rheological and surface properties of bentonite dispersions

In this study, the adsorption, bridging, and intercalation effects of a cationic surfactant, benzyldimethyltetradecyl ammonium chloride (BDTDACl), on bentonite clay suspensions was investigated. The adsorption, rheological behaviors, and colloidal properties of the clay dispersions were determined as a function surfactant concentration. Adsorption isotherms were obtained using the batch-equilibrium technique. The rheological behavior of the clay suspensions was obtained by shear stress-shear rate measurements within 0-350 s-1 shear rates. The structure of the composite particles was analyzed by using X-ray diffraction analysis and it was found that the expansions of basal d-spacings are less than 16.80 A, suggesting a monolayer structure.     

A biosensor based on urate oxidase-peroxidase coupled enzyme system for uric acid determination in urine

A new amperometric biosensor based on urate oxidase-peroxidase coupled enzyme system for the specific and selective determination of uric acid in urine was developed. Commercially available urate oxidase and peroxidase were immobilized with gelatin by using glutaraldehyde and fixed on a pretreated teflon membrane. The method is based on generation of H₂O₂ from urine uric acid by urate oxidase and its consuming by peroxidase and then measurement of the decreasing of dissolved oxygen concentration by the biosensor. The biosensor response depends linearly on uric acid concentration between 0.1 and 0.5 μM. In the optimization studies of the biosensor, phosphate buffer (pH 7.5; 50 mM) and 35 °C were obtained as the optimum working conditions. In addition, the most suitable enzyme activities were found as 64.9×10⁻³ U cm⁻² for urate oxidase and 512.7 U cm⁻² for peroxidase. And also some characteristic studies of the biosensor such as reproducibility, substrate specificity and storage stability were carried out.     

G-networks: a unifying model for neural and queueing networks

We survey results concerning a new stochastic network we have developed [1–7], which was initially motivated by neural network modelling [1], or — as we called it — by queueing networks with positive and negative customers [2, 3]. Indeed, it is well known that signals in neural networks are formed by impulses or action potentials, traveling much like customers in a queueing network. We call this model a G-network because it serves as a unifying basis for diverse areas of stochastic modelling in queueing networks, computer networks, computer system performance and neural networks. In its simplest version, negative and positive signals or customers circulate among a finite set of units, modelling inhibitory and excitatory signals of a neural network, or negative and positive customers of a queueing network. Signals can arrive either from other units or from the outside world. Positive signals are accumulated at the input of each unit, and constitute its signal potential. The state of each unit or neuron is its signal potential (which is equivalent to the queue length), while the network state is the vector of signal potentials at each neuron. If its potential is positive, a unit or neuron fires, and sends out signals to the other neurons or to the outside world. As it does so, its signal potential is depleted. In the Markovian case, this model has product form, i.e. the steady-state probability distribution of its potential vector is the product of the marginal probabilities of the potential at each neuron. The signal flow equations of the network, which describe the rate at which positive or negative signals arrive to each neuron, are non-linear. We discuss the relationship between this model and the usual connectionist (formal) model of neural networks, and present applications to combinatorial optimization and to image texture processing. Extensions of the model to the case of multiple signal classes, and to networks with triggered customer motion are presented. We also examine the general stability conditions which guarantee that the network has a well-defined steady-state behaviour.     

Relativistic energies of 1s2, 1s22s and 1s22s2 isoelectronic sequences in first- and second-row atoms

An accurate semi-empirical method based on the many-electron theory of Sinano?lu for calculating relativistic contributions to atomic energies is developed and applied to the 1s², 1s²2s and 1s²2s² isometric sequences in the first and second periods. Orbital additivity of relativistic energies is tested and found to be an inaccurate assumption for small Z.