Publications by authors named "Karthik Tangirala"

In the present work, we developed hybrid nanostructures based on ZnO films deposited on macroporous silicon substrates using the sol-gel spin coating and ultrasonic spray pyrolysis (USP) techniques. The changes in the growth of ZnO films on macroporous silicon were studied using a UV-visible spectrometer, an X-ray diffractometer (XRD), scanning electron microscopy (SEM) and atomic force microscopy (AFM). XRD analysis revealed the beneficial influence of macroporous silicon on the structural properties of ZnO films.

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Undoped and nickel-doped zinc oxide thin films were deposited on sodalime glass substrates by utilizing dip coating and ultrasonic spray pyrolysis deposition techniques. In both cases zinc acetate and nickel acetylacetonate were used as zinc precursor and nickel dopant source, respectively. XRD analysis confirms the ZnO wurtzite structure with (002) as the preferential orientation.

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In this study, we report a simple method for the fabrication of carbon dots sensitized zinc oxide-porous silicon (ZnO-pSi) hybrid structures for carbon dioxide (CO) sensing. A micro-/nanostructured layer of ZnO is formed over electrochemically prepared pSi substrates using a simple chemical precipitation method. The hybrid structure was structurally and optically characterized using scanning electron microscopy, X-ray diffraction, fluorescence, and cathodoluminescence after the incorporation of hydrothermally prepared nitrogen-doped carbon dots (NCDs) by drop casting.

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Pure and copper (Cu)-incorporated tin oxide (SnO₂) pellet gas sensors with characteristics provoking gas sensitivity were fabricated and used for measuring carbon monoxide (CO) atmospheres. Non-spherical pure SnO₂ nano-structures were prepared by using urea as the precipitation agent. The resultant SnO₂ powders were ball milled and incorporated with a transition metal, Cu, via chemical synthesis method.

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Machine learning algorithms are widely used to annotate biological sequences. Low-dimensional informative feature vectors can be crucial for the performance of the algorithms. In prior work, we have proposed the use of a community detection approach to construct low dimensional feature sets for nucleotide sequence classification.

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