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Kalagadda Venkateswara Rao

Researcher at Jawaharlal Nehru Technological University, Hyderabad

Publications -  32
Citations -  1160

Kalagadda Venkateswara Rao is an academic researcher from Jawaharlal Nehru Technological University, Hyderabad. The author has contributed to research in topics: Graphene & Wurtzite crystal structure. The author has an hindex of 13, co-authored 32 publications receiving 791 citations. Previous affiliations of Kalagadda Venkateswara Rao include Johns Hopkins University.

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X-Ray Analysis by Williamson-Hall and Size-Strain Plot Methods of ZnO Nanoparticles with Fuel Variation

TL;DR: In this paper, a simple and facile surfactant assisted combustion synthesis is reported for the ZnO nanoparticles, which is done with the assistance of non-ionic surfactants TWEEN 80.
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Synthesis of ZnO Nanoparticles by a Novel Surfactant Assisted Amine Combustion Method

TL;DR: In this article, the debye-Scherrer's and Williamson-Hall equations were used to calculate the size of ZnO nanopowders, which is in good agreement with the crystallite size calculated from X-Ray Diffraction pattern with the Particle Size Analyzer.
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Peptide-Based 68Ga-PET Radiotracer for Imaging PD-L1 Expression in Cancer.

TL;DR: A peptide-based imaging agent, [68Ga]WL12, is reported to detect PD-L1 expression in tumors noninvasively by positron emission tomography (PET) with high binding specificity and high tissue contrast in all tumor models tested.
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Comparative study on the mechanical, tribological, morphological and structural properties of vortex casting processed, Al–SiC–Cr hybrid metal matrix composites for high strength wear-resistant applications: Fabrication and characterizations

TL;DR: In this article, the characterization of Al-Si alloy-based metal matrix composites that are reinforced with silicon carbide and chromium is performed with the help of scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD), microhardness test, tensile test, sliding wear test, scratch test, and porosity analysis.
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Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm

TL;DR: An optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts and compares the efficacy of NNA over conventional optimization tools.