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1. Disentangling the effects of high permittivity materials on signal optimization and sample noise reduction via ideal current patterns

   https://doi.org/10.1002/mrm.27554  

   Vaidya, Manushka V. ; Sodickson, Daniel K. ; Collins, Christopher M. ; Lattanzi, Riccardo ( November 2018 , Magnetic Resonance in Medicine)

   To investigate how high‐permittivity materials (HPMs) can improve SNR when placed between MR detectors and the imaged body.

   We used a simulation framework based on dyadic Green’s functions to calculate the electromagnetic field inside a uniform dielectric sphere at 7 Tesla, with and without a surrounding layer of HPM. SNR‐optimizing (ideal) current patterns were expressed as the sum of signal‐optimizing (signal‐only) current patterns and dark mode current patterns that minimize sample noise while contributing nothing to signal. We investigated how HPM affects the shape and amplitude of these current patterns, sample noise, and array SNR.

   Ideal and signal‐only current patterns were identical for a central voxel. HPMs introduced a phase shift into these patterns, compensating for signal propagation delay in the HPMs. For an intermediate location within the sphere, dark mode current patterns were present and illustrated the mechanisms by which HPMs can reduce sample noise. High‐amplitude signal‐only current patterns were observed for HPM configurations that shield the electromagnetic field from the sample. For coil arrays, these configurations corresponded to poor SNR in deep regions but resulted in large SNR gains near the surface due to enhanced fields in the vicinity of the HPM. For very high relative permittivity values, HPM thicknesses corresponding to even multiples of λ/4 resulted in coil SNR gains throughout the sample.

   HPMs affect both signal sensitivity and sample noise. Lower amplitude signal‐only optimal currents corresponded to higher array SNR performance and could guide the design of coils integrated with HPM.

    

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2. Combined magnetic and electric field processing of polymer matrix composites for orthogonal control of hierarchical particle arrangements

   https://doi.org/10.1088/1361-665X/aca6be  

   Masud, Md Abdulla ; Papula, Dashiell ; Erol, Anil ; Edson, Connor ; Widdowson, Denise ; von Lockette, Paris ; Ounaies, Zoubeida ( January 2023 , Smart Materials and Structures)

   Properties of particulate-filled polymer matrix composites are highly dependent on the spatial position, orientation and assembly of the particles throughout the matrix. External fields such as electric and magnetic have been individually used to orient, position and assemble micro and nanoparticles in polymer solutions and their resulting material properties were investigated, but the combined effect of using more than one external field on the material properties has not been studied in detail. Applying different configurations of electric and magnetic fields on geometrically and magnetically anisotropic particulates can produce varying microarchitectures with a range of material properties. Experimentally and with simulations, we systematically probe the effect of combined electric and magnetic fields on the microstructure formation of geometrically and magnetically anisotropic barium hexaferrite (BHF) in polydimethylsiloxane (PDMS). The magnetic and dielectric properties resulting from different microstructures are characterized and microstructure-property relationships are analyzed. Our results demonstrate that a variety of microarchitectures can be produced using multi-field processing depending on the nature of the applied external field. For example, the application of an electric field creates macro-chains where the orientation of the BHF stacks inside the macro-chains is random. On the other hand, application of a magnetic field rotates the BHF stacks within the macro-chain in the direction dictated by the magnetic field. In simulations, the dielectrophoretic, magnetic, and viscous forces and torques acting on the particles show that particle anisotropies are central to the ability to control orientation along the orthogonal magnetic and geometric axes, mirroring experimental results. The authors refer to the ability to manipulate particle orientation along orthogonal axes as ‘orthogonal control’. Using this technique, not only are a variety of microstructures possible, but also a range of dielectric and magnetic properties can result. For example, for 1 vol% BHF-PDMS composites, the experimental dielectric permittivity is found to vary from 2.84 to 5.12 and the squareness ratio (remnant magnetization over saturation magnetization) is found to vary from 0.55 to 0.92 (from 0.52 to 0.99 in simulations) depending on the applied external stimuli. The ability to predict and produce a variety of microstructures with a range of properties from a single material set will be particularly beneficial for resin pool based additive manufacturing and 3D printing.

    

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3. Interfacial effects on the electrical behavior of elastomer nanoparticulate composites

   https://doi.org/10.1117/12.2515364  

   Meddeb, Amira ; Ounaies, Zoubeida ; Lopez-Pamies, Oscar ( March 2019 , SPIE 10968, Behavior and Mechanics of Multifunctional Materials XIII)

   Polymer nanocomposites exhibit unique effective properties that do not follow conventional effective media approaches. The nanoparticle-polymer interphase has been shown to strongly influence the nanocomposites behavior due o its significant volume when the particles are nano-sized, affording an opportunity to tune the dielectric response of the resulting nanocomposite. In this study, we investigate the effects of TiO2 nanoparticles on the electrical properties and the charges distribution and transport in polydimethylsiloxane (PDMS) nanocomposites. Impedance spectroscopy shows suppression of interfacial Maxwell-Wagner-Sillars (MWS) polarization accompanied by a reduction in the low frequency dielectric permittivity and loss at high temperatures in the presence of the TiO2 nanoparticles. Thermally stimulated discharge current measurements confirm that the suppression of the interfacial polarization relaxations happens by redistributing or depleting the charges through the composite and hindering their mobility, potentially resulting in lower electrical conduction and higher breakdown strength. Although the model materials investigated here are TiO2 nanoparticles and Sylgard 184 PDMS, our findings can be extended to other nanoparticulate-filled elastomer composites to design lightweight dielectrics, actuators and sensors with improved capabilities. 

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4. Nonlinear electrophoretic velocity of a spherical colloidal particle

   https://doi.org/10.1017/jfm.2023.537  

   Cobos, Richard ; Khair, Aditya S. ( August 2023 , Journal of Fluid Mechanics)

   Electrophoresis is the motion of a charged colloidal particle in an electrolyte under an applied electric field. The electrophoretic velocity of a spherical particle depends on the dimensionless electric field strength$\beta =a^*e^*E_\infty ^*/k_B^*T^*$, defined as the ratio of the product of the applied electric field magnitude$E_\infty ^*$and particle radius$a^*$, to the thermal voltage$k_B^*T^*/e^*$, where$k_B^*$is Boltzmann's constant,$T^*$is the absolute temperature, and$e^*$is the charge on a proton. In this paper, we develop a spectral element algorithm to compute the electrophoretic velocity of a spherical, rigid, dielectric particle, of fixed dimensionless surface charge density$\sigma$over a wide range of$\beta$. Here,$\sigma =(e^*a^*/\epsilon ^*k_B^*T^*)\sigma ^*$, where$\sigma ^*$is the dimensional surface charge density, and$\epsilon ^*$is the permittivity of the electrolyte. For moderately charged particles ($\sigma ={O}(1)$), the electrophoretic velocity is linear in$\beta$when$\beta \ll 1$, and its dependence on the ratio of the Debye length ($1/\kappa ^*$) to particle radius (denoted by$\delta =1/(\kappa ^*a^*)$) agrees with Henry's formula. As$\beta$increases, the nonlinear contribution to the electrophoretic velocity becomes prominent, and the onset of this behaviour is$\delta$-dependent. For$\beta \gg 1$, the electrophoretic velocity again becomes linear in field strength, approaching the Hückel limit of electrophoresis in a dielectric medium, for all$\delta$. For highly charged particles ($\sigma \gg 1$) in the thin-Debye-layer limit ($\delta \ll 1$), our computations are in good agreement with recent experimental and asymptotic results.

    

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5. A MACHINE LEARNING APPROACH FOR IMPACT DAMAGE QUANTIFICATION IN POLYMER MATRIX COMPOSITES

   https://doi.org/10.12783/asc37/36412  

   BERKOWITZ, KATHERINE ; GUHA, RISHABH D. ; OLUWAJIRE, OLUWATIMILEHIN ; GRACE, LANDON ( September 2022 , American Society for Composites)

   Largely due to superior properties compared to traditional materials, the use of polymer matrix composites (PMC) has been expanding in several industries such as aerospace, transportation, defense, and marine. However, the anisotropy and nonhomogeneity of these structures contribute to the difficulty in evaluating structural integrity; damage sites can occur at multiple locations and length scales and are hard to track over time. This can lead to unpredictable and expensive failure of a safety-critical structure, thus creating a need for non-destructive evaluation (NDE) techniques which can detect and quantify small-scale damage sites and track their progression. Our research group has improved upon classical microwave techniques to address these needs; utilizing a custom device to move a sample within a resonant cavity and create a spatial map of relative permittivity. We capitalize on the inevitable presence of moisture within the polymer network to detect damage. The differing migration inclinations of absorbed water molecules in a pristine versus a damaged composite alters the respective concentrations of the two chemical states of moisture. The greater concentration of free water molecules residing in the damage sites exhibit highly different relative permittivity when compared to the higher ratio of polymer-bound water molecules in the undamaged areas. Currently, the technique has shown the ability to detect impact damage across a range of damage levels and gravimetric moisture contents but is not able to specifically quantify damage extent with regards to impact energy level. The applicability of machine learning (ML) to composite materials is substantial, with uses in areas like manufacturing and design, prediction of structural properties, and damage detection. Using traditional NDE techniques in conjunction with supervised or unsupervised ML has been shown to improve the accuracy, reliability, or efficiency of the existing methods. In this work, we explore the use of a combined unsupervised/supervised ML approach to determine a damage boundary and quantification of single-impact specimens. Dry composite specimens were damaged via drop tower to induce one central impact site of 0, 2, or 3 Joules. After moisture exposure, Entrepreneur Dr, Raleigh, North Carolina 27695, U.S.A. 553 each specimen underwent dielectric mapping, and spatial permittivity maps were created at a variety of gravimetric moisture contents. An unsupervised K-means clustering algorithm was applied to the dielectric data to segment the levels of damage and define a damage boundary. Subsequently, supervised learning was used to quantify damage using features including but not limited to thickness, moisture content, permittivity values of each cluster, and average distance between points in each cluster. A regression model was trained on several samples with impact energy as the predicted variable. Evaluation was then performed based on prediction accuracy for samples in which the impact energies are not known to the model.

    

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