Studies on IPNS based on nitrile rubber and polyalkyl methacrylates

Sequential interpenetrating polymer networks (IPNs) based on nitrile rubber and various types of polyalkyl methacrylates such as poly(n-butyl methacrylate), poly(ethyl methacrylate), and poly(methyl methacrylate) were synthesized. The compositions of the IPNs could be varied by varying the reaction parameters such as swelling time and concentration of crosslinker. The tensile properties of the IPNs show that with increase in bulkiness of the ester group of the acrylates the tensile strength decreases, whereas elongation at break increases because of decreased stiffness of the acrylate phase. The dynamic modulus and loss tangent of the IPNs also show similar trend because of the above reason. All the IPNs were also tested for dynamic properties under multifrequency mode, and with the help of the WLF equation, the behavior of these IPNs in the frequency range of 1–10⁵ Hz were evaluated. The results showed reasonably high tan δ with good storage modulus in the entire frequency range for all the IPNs. © 1997 John Wiley & Sons, Inc. J Appl Polm Sci 65:549–554, 1997     

Coherent-interface-induced strain in large lattice-mismatched materials:A new approach for modeling Raman shift

Strain engineering as one of the most powerful techniques for tuning optical and electronic properties of Ⅲ-nitrides requires reliable methods for strain invest...     

Annulus pressurization load evaluation after high energy line break

In Light Water Reactor design, it is required by the US Nuclear Regulatory Commission (NRC) that a conservative method must be used to estimate the loads on the Reactor Pressure Vessel (RPV) and components in the analysis of the loss of coolant accident (LOCA). As part of the safety design of the containment for light water reactors, jet forces resulting from the postulated rupture of high-pressure piping are accounted for in the design of internal structures. Traditionally, the jet force was estimated using ANS 58.2. However, it does not address the compressibility, reflection, and feedback amplification of the pressure forces as directed by NRC. In this paper, a numerical method is developed to estimate the annulus pressurization caused by the high-energy line break in the Economic Simplified Boiling Reactor (ESBWR). The pressure loading time history caused by the blast wave was predicted by the method. From the prediction, the maximum pressure loading was obtained. The method and the results have been submitted to the NRC as the supporting documents for the ESBWR certification.     

Prediction of Fracture Parameters of High Strength and Ultra-High Strength Concrete Beams using Minimax Probability Machine Regression and Extreme Learning Machine

This paper deals with the development of models for prediction of facture parameters, namely, fracture energy and ultimate load of high strength and ultra high strength concrete based on Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM). MPMR is developed based on Minimax Probability Machine Classification (MPMC). ELM is the modified version of Single Hidden Layer Feed Foreword Network (SLFN). MPMR and ELM has been used as regression techniques. Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split tensile strength, notch depth, and modulus of elasticity and output is fracture energy and ultimate load A total of 87 data sets (input-output pairs) are used, 61 of which are used to train the model and 26 are used to test the models. The data-sets used in this study are derived from experimental results. A comparative study has been presented between the developed MPMR and ELM models. The results showed that the developed models give reasonable performance for prediction of fracture energy and ultimate load.     

Multivariate Adaptive Regression Splines Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. MARS model has been developed by establishing a relationship between a set of predicators and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Four MARS models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load.MARS has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, GF, KIC and CTODC are in good agreement with those of the experimental values.     

All solid supercapacitor based on activated carbon and poly [2,5-benzimidazole] for high temperature application

In the present work, we report high temperature performance of solid electrolyte supercapacitor based on activated carbon (AC) and phosphoric acid doped poly [2,5 benzimidazole] (ABPBI). Supercapacitors with varying concentrations of solid electrolyte in the electrode were fabricated and unit cells were analyzed over a wide temperature range of 27–120 °C. Supercapacitor with AC/ABPBI wt ratio 1.0:0.25 exhibited a specific capacitance of 197 F g^?1 at room temperature. To the best of our knowledge, the value reported here is one of the highest for electric double layer supercapacitor with a solid electrolyte. The specific capacitance of supercapacitors having various compositions increased with temperature. The specific capacitance for AC/ABPBI wt ratio 1.0:0.25, capacitance increased to 248 F g^?1 at 120 °C. The performance of supercapacitors was also analyzed by electrochemical impedance spectroscopy. The Nyquist plots at room temperature and 100 °C were studied by fitting them using Randles equivalent circuit. Supercapacitor with AC/ABPBI wt ratio 1.0:0.25 showed phase angle of 86.8° at low frequency which indicated excellent capacitive behavior at room temperature. The supercapacitor was found to have good stability during galvanostatic charge–discharge cycling even after repeated heating and cooling.     

Studies on the engineering properties of LCP‐Vectra B 950/PP blends with the variations of EAA content

Polypropylene (PP) was melt blended with Vectra B‐950 [a thermotropic liquid crystalline polymer (LCP)], in a single screw extruder in presence of different doses of ethylene acrylic acid (EAA) copolymer, as modifier. The effect of incorporation in different proportions of EAA at a fixed dose of 5% LCP, on mechanical, thermal, morphological, and rheological properties of such blends was studied and the same were compared with that of pure PP and amongst themselves. Mechanical analysis (tensile properties) of the prepared blends exhibited improvements in ultimate tensile strength (UTS), modulus, toughness, hardness, and impact strength of PP matrix with the incorporation of EAA. The improvement in mechanical properties is associated with the formation of LCP fibrils as evidenced by scanning electron microscopy (SEM). A strong interaction through H‐bonding between the segments of Vectra B‐950 and EAA was established by FTIR study. Differential scanning calorimetry (DSC) studies indicated substantial increase in melting point of the blends, and thermogravimetric analysis (TGA) showed that the thermal stability of PP was improved with the addition of LCP and EAA. Rheological properties showed that LCP and EAA drop down the melt viscosity of PP and thus facilitate processibility of blends. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Site Characterization Model Using Artificial Neural Network and Kriging

In this paper, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India. More than 2,700 field SPT values (N) has been collected from 766 boreholes spread over an area of 220-km2 area in Bangalore, India. To get N corrected value (Nc), N values have been corrected for different parameters such as overburden stress, size of borehole, type of sampler, length of connected rod. In three-dimensional analysis, the function Nc = Nc(X,Y,Z), where X, Y, and Z are the coordinates of a point corresponds to Nc value, is to be approximated with which Nc value at any half-space point in Bangalore, India can be determined. An attempt has been made to develop artificial neural network (ANN) model using multilayer perceptrons that are trained with Levenberg-Marquardt back-propagation algorithm. Also, a geostatistical model based on ordinary kriging technique has been adopted. The knowledge of the semivariogram of the Nc values is used in the ordinary kriging method to predict the Nc values at any point in the subsurface of Bangalore, India where field measurements are not available. The results obtained show that ANN model is fairly accurate in predicting Nc values. In case of ordinary kriging, a new type of cross-validation analysis shows that it is a robust model for prediction of Nc values. A comparison between the ANN and geostatistical model demonstrates that the ANN model is superior to Geostatistical model in predicting Nc values in the subsurface of Bangalore, India.     

Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles

This article adopts least square support vector machine (LSSVM) and multivariate adaptive regression spline (MARS) for prediction of lateral load capacity (Q) of pile foundation. LSSVM is firmly based on the theory of statistical learning, uses regression technique. MARS is a nonparametric regression technique that models complex relationships. Diameter of pile (D), depth of pile embedment (L), eccentricity of load (e), and undrained shear strength of soil (S _u) have been used as input parameters of LSSVM and MARS. Equations have been presented from the developed MARS and LSSVM. This study also presents a comparative study between the developed MARS and LSSVM.     

Synthesis and characterization of azo‐based methacrylate polymers with methoxy and nitro end groups for nonlinear optical applications

One new and three already described azobased methacrylate monomers with methoxy and nitro end groups and spacer length 2 and 6 were synthesized. These monomers were copolymerized with methyl methacrylate and the monomers as well as copolymers were characterized by classical spectroscopy techniques (FTIR, NMR, and UV‐VIS), gel permeation chromatography (GPC), elemental analysis and thermal analysis (TGA and DSC). The glass transition temperature of the polymers was found to be above room temperature and thermal decomposition temperatures above 100°C. All the polymers were amorphous in nature and formed excellent homogeneous films with good optical transparency. The polymer films coated on indium tin oxide glass slides were poled and their order parameters were calculated to check the stability of oriented dipoles. Few samples were also studied for their second harmonic generation properties. Temporal stability, checked up to 120 h at room temperature, was found to be quite satisfactory. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 3497–3504, 2007