Reinforcement of polymers with multiple inclusions of varying length scales and morphologies enable enhancement and tailorability of thermo-mechanical properties in resulting polymers. Computational material models can eliminate the trial-and-error approach of developing these hybrid reinforced polymers, enable prediction of interphase properties, and allow virtual exploration of design space. In this work, computational models, specifically representative volume elements were developed for acrylonitrile butadiene styrene polymer reinforced with nanoscale iron oxide particles and micro-scale short carbon fibers. These representative volume elements were used to predict the tensile modulus of resulting polymer nanocomposite with varying particle concentrations, orientations, interphases, and clustering to realistically replicate the actual material as observed in optical and electron microscopy. The interphase elastic modulus was obtained through established analytical formulations and incorporated into the representative volume elements by defining an interphase region around the reinforcements. The tensile modulus estimated using representative volume elements agreed well with the experiments, evidently showing that the effective tensile modulus of the polymer nanocomposite increased with increase in interphase thickness, aspect ratio, and particle content. Clustering was only observed in Fe3O4 nanoparticles but its size did not have any effect on the effective tensile modulus. The developed computational modeling framework and the resultant prediction of tensile modulus offers a design path which can be extended to other polymer nanocomposites containing multiple inclusions.

Two resonance-based methods for magneto-dielectric material characterization are presented in this paper using stepped impedance resonators (SIR). A single SIR (SSIR) is initially used to create two spatially separated sensing areas for permittivity and permeability measurements predicated on the resonance frequency variation. Next, a differential-based sensing method, based on resonance frequency splitting, is introduced for permittivity and permeability differential measurements. For this purpose, four spatially separated sensing areas are established on a single resonance structure using a double-stepped impedance resonator (DSIR). The presented sensors provide a reliable sensing method for magneto-dielectric characterization and comparison. The proposed sensing methods are validated through numerical analysis based on electromagnetic simulation and experimental measurement of materials with different intrinsic electromagnetic properties.

Thermoplastics polymers like Acrylonitrile Butadiene Styrene (ABS) are often reinforced with nano/micro reinforcements to enhance their mechanical, thermal and electrical properties. However, the viscoelastic nature of these polymers results in their strong dependence on the applied strain rate and temperature sensitivity, leading to their characterization complexity. Hence it is paramount to study the strain rate-dependent mechanics of neat ABS. In this study, the effect of strain rate and temperature on Young’s modulus of ABS polymer was characterized using a dynamic mechanical analyzer (DMA). Storage modulus curves at various temperatures and frequencies were transformed into a representative master curve at a specific temperature using the time-temperature superposition (TTS) principle. Based on this curve‘s storage modulus and frequency relation, an empirical fit function was developed and the strain rate values were extrapolated. Using integral relations of viscoelasticity, the results were further transformed to a time domain relaxation function to extract the strain rate-sensitive Young’s modulus at different loading rates. This method was validated by comparing the data with tensile tests conducted on ABS coupons as per ASTM D638-14. The results were acceptable over a wide range of strain rates and indicated a clear sensitivity of ABS to strain rate and temperature. The strategy used in this work can be employed to study the effect of reinforcement morphology in ABS thermoplastics using DMA.

Metamaterials are engineered periodic structures designed to have unique properties not encountered in naturally occurring materials. One such unusual property of metamaterials is the ability to exhibit negative refractive index over a prescribed range of frequencies. A lens made of negative refractive index metamaterials can achieve resolution beyond the diffraction limit. This paper presents the design of a metamaterial lens and its use in far-field microwave imaging for subwavelength defect detection in nondestructive evaluation (NDE). Theoretical formulation and numerical studies of the metamaterial lens design are presented followed by experimental demonstration and characterization of metamaterial behavior. Finally, a microwave homodyne receiver-based system is used in conjunction with the metamaterial lens to develop a far-field microwave NDE sensor system. A subwavelength focal spot of size 0.82λ was obtained. The system is shown to be sensitive to a defect of size 0.17λ × 0.06λ in a Teflon sample. Consecutive positions of the defect with a separation of 0.23λ was resolvable using the proposed system.