Reflection and refraction of electromagnetic waves by artificial periodic composites (metamaterials) can be accurately modeled by an effective medium theory only if the boundary of the medium is explicitly taken into ...
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Reflection and refraction of electromagnetic waves by artificial periodic composites (metamaterials) can be accurately modeled by an effective medium theory only if the boundary of the medium is explicitly taken into account and the two effective parameters of the medium (the index of refraction and the impedance) are correctly determined. Theories that consider infinite periodic composites do not satisfy the above condition. As a result, they can not model reflection and transmission by finite samples with the desired accuracy and are not useful for design of metamaterial-based devices. As an instructive case in point, we consider the “current-driven” homogenization theory, which has recently gained popularity. We apply this theory to the case of one-dimensional periodic medium wherein both exact and homogenization results can be obtained analytically in closed form. We show that, beyond the well-understood zero-cell limit, the current-driven homogenization result is inconsistent with the exact reflection and transmission characteristics of the slab.
Nematic and cholesteric liquid crystals are three-dimensional fluids that possess long-range orientational ordering and can support both topological defects and chiral superstructures. Implications of this ordering re...
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Nematic and cholesteric liquid crystals are three-dimensional fluids that possess long-range orientational ordering and can support both topological defects and chiral superstructures. Implications of this ordering remain unexplored even for simple dynamic processes such as the ones found in so-called “fall experiments,” or motion of a spherical inclusion under the effects of gravity. Here we show that elastic and surface anchoring interactions prompt periodic dynamics of colloidal microparticles in confined cholesterics when gravity acts along the helical axis. We explore elastic interactions between colloidal microparticles and confining surfaces as well as with an aligned ground-state helical structure of cholesterics for different sizes of spheres relative to the cholesteric pitch, demonstrating unexpected departures from Stokes-like behavior at very low Reynolds numbers. We characterize metastable localization of microspheres under the effects of elastic and surface anchoring periodic potential landscapes seen by moving spheres, demonstrating the important roles played by anchoring memory, confinement, and topological defect transformation. These experimental findings are consistent with the results of numerical modeling performed through minimizing the total free energy due to colloidal inclusions at different locations along the helical axis and with respect to the confining substrates. A potential application emerging from this work is colloidal sorting based on particle shapes and sizes.
We introduce a label-free method for detecting high-grade prostatic intraepithelial neoplasia (HGPIN) in unstained biopsies. We image this condition based on the identification of basal cells in biopsies that otherwis...
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Ochratoxin-A[7-(L-β-phenylalanylcarbonyl)-carboxyl-5-chloro-8-hydroxy-3,4-dihydro-3R-methyl-isocumarin, OTA] is a common food contaminant mycotoxin that enters the human body through the consumption of improperly sto...
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Ochratoxin-A[7-(L-β-phenylalanylcarbonyl)-carboxyl-5-chloro-8-hydroxy-3,4-dihydro-3R-methyl-isocumarin, OTA] is a common food contaminant mycotoxin that enters the human body through the consumption of improperly stored food products. Upon ingestion, it leads to immuno-suppression and immuno-toxicity. OTA has been known to produce nephrotoxic, teratogenic, and carcinogenic activity (via oxidative DNA damage) in several species. This review introduces potentials of electrochemical biosensor to provide breakthroughs in OTA detection through improved selectivity and sensitivity and also the current approaches for detecting OTA in food products.
Significant physical challenges remain for CMOS technology to decrease I off as transistor dimension and power supply voltages continue downscaling. However, a fundamental thermodynamic limit in the subthreshold slop...
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Significant physical challenges remain for CMOS technology to decrease I off as transistor dimension and power supply voltages continue downscaling. However, a fundamental thermodynamic limit in the subthreshold slope SS = |(∂V g )/(∂lnI d )| = ln10 · k B T/q at >60 mV/dec exists at room temperature. We have designed and demonstrated the first semiconductor nanowires (NWs) and nanoelectromechanical system (NEMS) field effect transistor structure (NW-NEMFET). We have previously demonstrated 0.5 ps intrinsic delay and near ballistic operation in quantum confined semiconductor heterostructure NWFETs with diameters less than 15 nm.[1] The current design uses high performance suspended semiconductor NWs as the conduction channel, while the electrostatic pull-in of the NW towards the gate stack enables abrupt switching to the off-state leading to high frequency, low power nanoelectronics. Simulation shows that compared to planar suspended-gate FET (SGFET) design [2], NW-NEMFET allows zero SS with 10 15 on-off ratio and near 1V pull-in voltage due to enhanced 3D capacitive coupling, as well as operation at very-high-frequency (VHF) and even ultra-high-frequency (UHF) due to the NW beams high aspect ratio and small dimensions.
Multi-hole defect (MHD) photonic crystal cavities functionalized with in situ synthesized DNA bioreceptors are demonstrated for biosensing applications. By significantly increasing light interaction with target biomol...
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We demonstrate the superior inductive properties of coiled carbon nanotubes (CCNTs) through numerical computation and analytical modeling, for the next generation of nanoscale, on-chip inductors. Taking advantage of t...
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We demonstrate the superior inductive properties of coiled carbon nanotubes (CCNTs) through numerical computation and analytical modeling, for the next generation of nanoscale, on-chip inductors. Taking advantage of the kinetic inductance (Lk), particularly evident at the nanoscale we find that the inductance can be increased by three orders of magnitude through changing the tube radius as well as the coil radius while the device footprint of the CCNTs can be reduced by 60%. By varying the geometric parameters of the coiled structure, the external magnetic inductance (LM,ext) can be as high as 20% of the Lk. We also report that the self resonant frequency (fSR) of CCNTs can be as much of the order of THz whereas the fSR of conventional copper(Cu) spiral inductors are limited to around 40GHz. Moreover when the material volume is considered, CCNTs have the potential to achieve Quality Factor (Q) eight times as Cu and when the footprint volume is considered Q can be twice as Cu All these promising properties of CCNTs make them a potential candidate for the entire frequency spectrum.
This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons th...
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This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.
This paper presents the design and implementation of highly-miniaturized, low-power CMOS signal conditioning schemes intended for use in a totally implantable biomedical sensor platform. Due to the thrust for the deve...
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This paper presents the design and implementation of highly-miniaturized, low-power CMOS signal conditioning schemes intended for use in a totally implantable biomedical sensor platform. Due to the thrust for the development implantable biomedical sensing systems for health management and disease prevention, there exists a need for signal processing schemes which occupy very little on-chip real estate and consume negligible amounts of power. In light of this, this paper presents both a CMOS current-to-frequency converter and voltage-to-frequency converter which have been designed primarily for use in implantable biosensing platforms and applications. Such designs can be implemented in stand-alone single sensor designs, or in tandem to create multi-analyte architectures. The versatility of employing current-to-frequency as well as voltage-to-frequency signal transduction schemes presents an avenue for the integration with any electrochemical sensing element which has been fabricated in an amperometric or voltammetric fashion. Furthermore, we demonstrate the efficacy of both these circuit designs by integrating them together with high performance electrochemical implantable glucose and pH sensors. The low power consumption and miniature size of the amperometric and voltammetric signal processing units (0.25 mm 2 and 18 μW / 0.045 mm 2 and 122 μW, respectively) presents an ideal design for signal processing in implantable continuous metabolic monitoring devices.
Pervasive and wearable brain-computer interface (BCI) systems show great potential for effectively understanding human mental activities and intentions in their daily life. In this paper, we propose a real-time BCI sy...
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Pervasive and wearable brain-computer interface (BCI) systems show great potential for effectively understanding human mental activities and intentions in their daily life. In this paper, we propose a real-time BCI system based on mobile devices and cloud computing to detect and recognize the user's mental states. A proof-of-concept prototype is developed based on a wearable, commercially available EEG headset, an Android smartphone, and a multi-core computing server. We demonstrate an integrated Android app containing three built-in functional modules. Specifically, a graphical window can receive and display continuous EEG data acquired from the headset in a real-time manner; a facial expression interface can indicate the user's mental states according to the analysis of EEG data on the server; and a retrospective analysis tool to investigate the mental behaviors over a long period of time.
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