Spray dryer processed graphene oxide/reduced graphene oxide for high-performance supercapacitor

This study deals with the utility of mini spray dryer process to improve the dispersibility, of graphene oxide(GO) and its application for high-performance supercapacitor. Initially, the neutral solution of GO was obtained using the modified Hummer's method. After this, the prepared GO solution was processed by mini spray dryer to obtain a more purified, lighter, and dispersed form of GO which is named as spray dryer processed GO (SPGO). The SPGO thus obtained showed excellent dispersibility behavior with various solvents, which is not found in case of conventional oven drying. Furthermore, utility of SPGO and its reduced form (r-SPGO) for supercapacitor applications have been investigated. Results obtained from the cyclic voltammetry(CV) analysis, impedance, and charge-discharge behavior of supercapacitor fabricated using r-SPGO shows enhanced features. Therefore, the simple spray dried GO and its reduced form, that is, r-SPGO can be utilized as a potential candidate for the supercapacitor application. Herein, as synthesized SPGO exhibited the specific capacitance of 12.07 and 37.6 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively, at a scan rate of 5 mV/s. On the other hand, reduced form of SPGO, that is, r-SPGO showed the specific capacitance of 27.16 and 230 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively.     

Mechanophysical Cues in Extracellular Matrix Regulation of Cell Behavior

The extracellular matrix (ECM) is a macromolecular network that can provide biochemical and structural support for cell adhesion and formation. It regulates cell behavior by influencing biochemical and physical cues. It is a dynamic structure whose components are modified, degraded, or deposited during connective tissue development, giving tissues strength and structural integrity. The physical properties of the natural ECM environment control the design of naturally or synthetically derived biomaterials to guide cell function in tissue engineering. Tissue engineering is an important field that explores physical cues of the ECM to produce new viable tissue for medical applications, such as in organ transplant and organ recovery. Understanding how the ECM exerts physical effects on cell behavior, when cells are seeded in synthetic ECM scaffolds, is of utmost importance. Herein we review recent findings in this area that report on cell behaviors in a variety of ECMs with different physical properties, i.e., topology, geometry, dimensionality, stiffness, and tension.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

Effect of ionic content,solid content,degree of neutralization,and chain extension on aqueous polyurethane dispersions prepared by prepolymer method

There are many variables in the preparation of aqueous polyurethane (PU) dispersions. Carboxylic acid content, solid content, degree of pre/postneutralization of the carboxylic acids, and chain extension all impact dispersion particle size, viscosity, pH, molecular weights, and glass transition temperature. This study evaluated the impact of these variables on a given PU dispersion formulation prepared from isophorone diisocyanate, an aliphatic polyester polyol, dimethylol propionic acid, and hexamethylene diamine with triethyl amine as the neutralizing base and N‐methyl pyrrolidone as the cosolvent. Changes in carboxylic acid content, degree of preneutralization, and chain extension were found to have the expected impacts on dispersions properties. Increased ionic content in the dispersion step led to lower particle size and higher viscosity, increased chain extension with its concurrent increase in molecular improved subsequent film properties. Surprising results were obtained by varying the amount of postneutralization and from increased solids content at the time of dispersion. Unexpectedly, both of these variations led to much higher dispersion viscosities and particle size in solution. To have these changes take place, it is theorized that there is a major change in solution morphology caused by these modifications. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 2514–2520, 2005     

Resource contention in shared-memory multiprocessors: A parameterized performance degradation model

A standard metric conventionally employed to compare the performance of different multiprocessor systems is speedup. Although providing a measure of the improvement in execution speed achievable on a system, this metric does not yield any insight into the factors responsible for limiting the potential improvement in speed. This paper studies the performance degradation in shared-memory multiprocessors as a result of contention for shared-memory resources. A replicate workload framework with a flexible mechanism for workload specification is proposed for measuring performance. Two normalized performance metrics—efficiency and overhead factor—are introduced to quantify the factors limiting performance and facilitate comparison across architectures. Finally, the proposed model is employed to measure and compare the performance of three contemporary shared-memory systems, with special emphasis on the newly released BBN Butterfly-II (TC2000), currently undergoing Beta test.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.