Microwave-induced combustion process variables for MgO nanoparticle synthesis using polyethylene glycol and sorbitol

In this study, MgO nanoparticles were prepared by microwave-induced combustion synthesis of magnesium nitrate (as oxidant and MgO precursor) and polyethylene glycol and sorbitol as fuels. The effects of various parameters including the aging time of the precursor gel before combustion, microwave power and fuel-to-oxidant ratio were investigated. Microwave was applied to homogeneously heat the precursor gel. The temperature profile and the composition of the released gases during the combustion process and the carbonaceous deposits on the MgO nanoparticles were studied by FTIR, TGA, Raman spectroscopy, and temperature programmed oxidation (TPO). The MgO samples were calcined at 400 °C for 3 h and characterized using SEM, TEM, BET and PXRD. MgO nanoparticles as small as 4.1 nm were formed using 20% excess of sorbitol and 40% excess of polyethylene glycol as the fuels.     

Enhancement of the properties of EPDM/NBR elastomers using nanoclay for seal applications

In this research, we have investigated the effects of addition of different percentages of nanoclay to the ethylene propylene diene monomer (EPDM) and nitrile butadiene rubber (NBR) on the characteristics of these rubbers as seal material. Properties such as tensile strength, modulus at different extensions, elongation at break, compressive set, hardness, and permeability and abrasion resistance are tested to assess the effect of addition of the nanoclay. Results indicate that addition of nanoclay at certain compositions could slightly reduce the strength of the rubber. However more stable modulus at different strains are provided, the hardness of the rubber is preserved and slightly enhanced, the permeability is reduced in both rubbers especially considerable decrease in EPDM is observed which is desirable in diminishing the effect of explosive decompression. At the same time the compression test shows that the nanoclay improves the performance of the rubbers under compression which is essential in seal application. The X‐ray diffraction tests clarify that the dispersion of the nanoclay in the NBR samples is of high quality. In the EPDM samples, the dispersion is in need of improvement. POLYM. COMPOS., 2009. © 2009 Society of Plastics Engineers     

Study of the morphology and granulometry of polyethylene–clay nanocomposite powders

Medium‐density polyethylene (MDPE) matrix nanocomposites reinforced with different clay contents were produced by using a planetary ball mill. The morphology of all the milled samples, including pure polyethylene and the polyethylene–clay nanocomposites, was examined by scanning electron microscopy. To investigate the effects of milling time and clay content on the particle size of polyethylene powder, sieve analysis was used. The results showed that during milling, the regular shape of pure polyethylene powder converts into flake shapes and the average particle size of the powder increases upon increasing the milling time because the welding mechanism is predominant. Also, the presence of the clay can increase the maximum‐particle size, and increasing the clay content can decrease the milling time required to reach the same maximum particle size. J. VINYL ADDIT. TECHNOL., 2010. © 2009 Society of Plastics Engineers     

A study in facial regions saliency: a fuzzy measure approach

People recognize familiar faces in a similar way by using interior facial features (facial regions) such as eyes, nose, mouth, etc. However, the importance of these regions in the realization of face identification and a quantification of the impact of such regions on the recognition process could vary from one region to another. An intuitively appealing observation is that of monotonicity: the more regions are taken into account in the recognition process, the better. From a formal point of view, the relevance of the facial regions and an aggregation of these pieces of experimental evidence can be described in the formal setting of fuzzy measures. Fuzzy measures are of particular interest with this regard given their monotonicity property (which stands in a clear contrast with the more restrictive additivity property inherent to probability–like measures). In this study, we concentrate on the construction of fuzzy measures (more specifically, $ \lambda $ -fuzzy measure) and characterize their performance in the problem of face recognition using a collection of experimental data.     

Synthesis of cactus-like zincoxysulfide (ZnOxS1−x) nanostructures assisted by a task-specific ionic liquid and their photocatalytic activities

A hydrothermal method has been employed to prepare cactus-like zincoxysulfide ZnO_xS_1?x nanostructures with the assistance of a dicationic task-specific ionic liquid (TSIL), [mim]{(CH)₂}₃[imm](SCN)₂. To the best of our knowledge, this is the first time that this TSIL with the SCN anion has been used in place of conventional reagents as a source of S to prepare a ZnO_xS_1?x nanostructure. The photocatalytic activities of the ZnO_xS_1?x nanostructures have been compared using UV and visible lights. BET results showed that the surface areas and photocatalytic activities of cactus-like zincoxysulfide ZnO_xS_1?x nanostructures were higher than those of other samples. ZnO_xS_1?x nanostructures with cactus-like morphology exhibited a significant enhancement of photocatalytic activity toward the degradation of methyl orange (MO) as compared to other samples, as revealed by photoluminescence measurements. This could be attributed to enhanced oxygen vacancies and crystallite defects formed as a result of substitution of S^2? in the lattice of ZnO.     

Constrained Bayes estimation in small area models with functional measurement error

In survey sampling, policy decisions regarding allocation of resources to subgroups, called small areas, or determination of subgroups with specific properties in a population are based on reliable estimates of small area parameters. However, the information is often collected at a different scale than these subgroups. Hence, we need to estimate characteristics of subgroups based on the coarser scale data. One of the main interests in small area estimation is to produce an ensemble of small area parameters whose distribution across small areas is close to the corresponding distribution of true parameters. In this paper, we consider the unit-level nested error linear regression model which is commonly used in small area estimation. We study the case where the covariate in the model is assumed to have measurement error. To study this complex model, we propose to use constrained Bayes method to estimate the true covariate to build the small area Bayes predictor. We also provide some measures of performance such as sensitivity, specificity, and positive/negative predictive values for the constructed Bayes predictor. We estimate the model parameters using the method of moments and Bayesian approach to get corresponding empirical and hierarchical Bayes predictors. The performance of our proposed approach is evaluated through a simulation study and a real data application.