Commenced in January 2007
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Edition: International
Paper Count: 3

Search results for: N. Rajeswara Rao

3 Effect of Gamma Irradiation on Structural and Optical Properties of ZnO/Mesoporous Silica Nanocomposite

Authors: K. Sowri Babu, P. Srinath, N. Rajeswara Rao, K. Venugopal Reddy


The effect of gamma ray irradiation on morphology and optical properties of ZnO/Mesoporous silica (MPS) nanocomposite was studied. The ZnO/MPS nanocomposite was irradiated with gamma rays of doses 30, 60, and 90 kGy and dose-rate of irradiation was 0.15 kGy/hour. Irradiated samples are characterized with FE-SEM, FT-IR, UV-vis, and Photoluminescence (PL) spectrometers. SEM pictures showed that morphology changed from spherical to flake like morphology. UV-vis analysis showed that the band gap increased with increase of gamma ray irradiation dose. This enhancement of the band gap is assigned to the depletion of oxygen vacancies with irradiation. The intensity of PL peak decreased gradually with increase of gamma ray irradiation dose. The decrease in PL intensity is attributed to the decrease of oxygen vacancies at the interface due to poor interface and improper passivation between ZnO/MPS.

Keywords: ZnO nanoparticles, nanocomposites, mesoporous silica, photoluminescence

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2 The Effect of Gamma rays on Physicochemical Properties of Carboxymethyl Starch

Authors: N. Rajeswara Rao, T. Venkatappa Rao, K. Sowri Babu, N. Srinivas Rao, P. S. V. Shanmukhi


Carboxymethyl Starch (CMS) is a biopolymer derived from starch by the substitution method. CMS is proclaimed to have improved physicochemical properties than native starch. The present work deals with the effect of gamma radiation on the physicochemical properties of CMS. The samples were exposed to gamma irradiation of doses 30, 60 and 90 kGy. The resultant properties were studied with electron spin resonance (ESR), fourier transform infrared spectrometer (FTIR), differential scanning calorimeter (DSC), X-ray diffractometer (XRD) and scanning electron microscopy. Irradiation of CMS by gamma rays initiates cleavage of glucosidic bonds producing different types of radicals. Some of these radicals convert to peroxy radicals by abstracting oxygen. The ESR spectrum of CMS is anisotropic and is thought to be due to the superposition of various component spectra. In order to analyze the ESR spectrum, computer simulations were also employed. ESR spectra are also recorded under different conditions like post-irradiation times, variable temperatures and saturation behavior in order to evaluate the stability of free radicals produced on irradiation. Thermal studies from DSC depict that for CMS the gelatization process was absconded at higher doses. Relative crystallinity was reduced significantly after irradiation from XRD Studies. FTIR studies also confirm the same aspect. From ESR studies, it was concluded that irradiated CMS could be a potential reference material in ESR dosimetry.

Keywords: gamma rays, free radicals, ESR simulations, gelatization

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1 Classification of Land Cover Usage from Satellite Images Using Deep Learning Algorithms

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao


Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 56