Although nanoparticle-modified polymers have tremendous promise in many applications, particularly dielectric energy storage, true nanoscale dispersion is extremely difficult to achieve. In this paper, we carefully engineer various dispersion states of titania nanoparticles in polyvinylidene fluoride and analyze their impact on dielectric behavior and energy storage ability. In particular, we compare nanocomposites prepared using commercially available nanoparticles to those we prepared using in situ and ex situ synthesis of nanoparticles. SEM and TEM studies showed that the in situ case leads to the best dispersion. Interestingly, dielectric permittivity was most influenced by dispersion state where the in situ case showed a higher increase, however, dielectric breakdown and energy storage density were less affected by dispersion and more affected by procedure that minimized residues and impurities. The in situ technique, in particular, showed nanoscale dispersion, low dielectric loss and higher energy storage density. In terms of mechanical behavior, all three cases showed a similar performance in the rubbery region, whereas the impact of dispersion was more pronounced in the glassy region. In fact, the trend was opposite to the dielectric permittivity where nanoscale dispersion resulted in a lower storage modulus likely due to the lower effective mechanical load transfer going to the nanoscale. The results of our study shed some light on the role of dispersion quality and processing techniques in affecting the final dielectric, mechanical and breakdown behavior of TiO 2 -based polymer nanocomposites.
Summary Surfactant floods can attain high oil recovery if optimal conditions with ultralow interfacial tensions (IFT) are achieved in the reservoir. A recently developed equation-of-state (EoS) phase-behavior net-average-curvature (NAC) model based on the hydrophilic-lipophilic difference (HLD-NAC) has been shown to fit and predict phase-behavior data continuously throughout the Winsor I, II, III, and IV regions. The state-of-the-art for viscosity estimation, however, uses empirical nonpredictive based on of fits to salinity scans, even though other parameters change, such as the phase number and compositions. In this paper, we develop the first-of-its-kind microemulsion viscosity model that gives continuous viscosity estimates in composition space. This model is coupled to our existing HLD-NAC phase-behavior EoS. The results show that experimentally measured viscosities in all Winsor regions (two- and three-phase) are a function of phase composition, temperature, pressure, salinity, and the equivalent alkane carbon number (EACN). More specifically, microemulsion viscosities associated with the three-phase invariant point have an M shape as formulation variables change, such as from a salinity scan. The location and magnitude of viscosity peaks in the M are predicted from two percolation thresholds after tuning to viscosity data. These percolation thresholds as well as other model parameters change linearly with EACN and brine salinity. We also show that the minimum viscosity in the M shape correlates linearly with EACN or the viscosity ratio. Other key parameters in the model are also shown to linearly correlate with the EACN and brine salinity. On the basis of these correlations, two- and three-phase microemulsion viscosities are determined in five-component space (surfactant, two brine components, and two oil components) independent of flash calculations. Phase compositions from the EoS flash calculations are entered into the viscosity model. Fits to experimental data are excellent, as well as viscosity predictions for salinity scans not used in the fitting process.
To attain an ongoing electricity economy, developing novel widespread electricity supply systems based on diverse energy resources are critically important. Several photovoltaic (PV) technologies exist, which cause various pathways to produce electricity from solar energy. This paper evaluates the competition between three influential solar technologies based on photovoltaic technique to find the optimal pathways for satisfying the electricity demand: (1) multicrystalline silicon; (2) copper, indium, gallium, and selenium (CIGS); and (3) multijunction. Besides the technical factors, there are other effective parameters such as cost, operability, feasibility, and capacity that should be considered when assessing the different pathways as optimal and viable long-term alternatives. To aid this decision-making process, a generic optimization-based model was developed for the long-range energy planning and design of future electricity supply system from solar energy. By applying dynamic programming techniques, the model is capable of identifying the optimal investment strategies and integrated supply system configurations from the many alternatives. The features and capabilities of the model were shown through application to Iran as a case study.
Dielectric elastomers are a class of electro-active polymers (EAPs) which have great potentials to be used in smart composites. These materials with compliant electrodes are converters of electrical energy to mechanical energy in order to produce external load and strain with good efficiency. Electrode materials should typically have good compliancy so that undergo large strain alongside the film, without producing any additional stress and constraint for the actuator. In the present study, the effect of 4 different electrode materials (graphite filled silicone oil, silver filled grease, graphite powder and electrically conductive silicone rubber) on the performance of dielectric elastomer actuators has been studied. The principle of operation, the method of fabrication and test method of planar actuators are discussed. We have also studied the effects of different driving voltages and different prestrain values on the actuator response. Experimental results showed that electrical conductivity, material compliancy, and compatibility with substrate in the electrode materials are some of the important parameters affecting the actuator performance.
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