In this work, we leverage graphene's unique tunable Seebeck coefficient for the demonstration of a graphene-based thermal imaging system. By integrating graphene based photothermo-electric detectors with micromachined silicon nitride membranes, we are able to achieve room temperature responsivities on the order of ~7-9 V/W (at λ = 10.6 μm), with a time constant of ~23 ms. The large responsivities, due to the combination of thermal isolation and broadband infrared absorption from the underlying SiN membrane, have enabled detection as well as stand-off imaging of an incoherent blackbody target (300-500 K). By comparing the fundamental achievable performance of these graphene-based thermopiles with standard thermocouple materials, we extrapolate that graphene's high carrier mobility can enable improved performances with respect to two main figures of merit for infrared detectors: detectivity (>8 × 10(8) cm Hz(1/2) W(-1)) and noise equivalent temperature difference (<100 mK). Furthermore, even average graphene carrier mobility (<1000 cm(2) V(-1) s(-1)) is still sufficient to detect the emitted thermal radiation from a human target.
The electrical properties of biological cells have connections to their pathological states. Here we present an electric impedance microflow cytometry (EIMC) platform for the characterization of disease states of single cells. This platform entails a microfluidic device for a label-free and non-invasive cell-counting assay through electric impedance sensing. We identified a dimensionless offset parameter δ obtained as a linear combination of a normalized phase shift and a normalized magnitude shift in electric impedance to differentiate cells on the basis of their pathological states. This paper discusses a representative case study on red blood cells (RBCs) invaded by Plasmodium falciparum malaria parasites. Invasion of P. falciparum induces physical and biochemical changes on the host cells throughout a 48-h multi-stage life cycle within the RBC. As a consequence, it also induces progressive changes in electrical properties of the host cells .We demonstrate that the EIMC system in combination with data analysis involving the new offset parameter allows differentiation of Pf–invaded RBCs from uninfected RBCs as well as among different P. falciparum intraerythrocytic asexual stages including the ring stage. The representative results provided here also point to the potential of the proposed experimental and analysis platform as a valuable tool for non-invasive diagnostics of a wide variety of disease states and for cell separation.
Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively.
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