X-Ray Peak Broadening Analysis and Optical Studies of ZnO Nanoparticles Derived by Surfactant Assisted Combustion Synthesis

In this paper, synthesis of ZnO nanoparticles is done by a simple and facile surfactant assisted combustion synthesis. The synthesis of ZnO nanoparticles has been prepared using Zinc nitrate as a precursor material, glycine as a fuel with the support of non-ionic surfactant TWEEN 80. The obtained ZnO nanoparticles have been studied using characterization techniques like X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), and UV-Vis Spectroscopy. XRD results reveal that the sample is crystalline with a hexagonal wurtzite phase. X-ray peak broadening analysis was used to evaluate the crystallite sizes and lattice strain by the Williamson-Hall (W-H) analysis. Further appropriate physical parameters such as strain, stress, and energy density values were also calculated using W-H analysis with different models, viz, uniform deformation model, uniform deformation stress model and uniform deformation energy density model. Transmission electron microscopy (TEM) result reveals that the ZnO nanoparticles sample is spherical in shape showing particle sizes less than 40 nm. The optical properties of ZnO nanoparticles were studied by UV-Vis spectroscopy. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3102

ASSESSMENT OF EARLY SEASON AGRICULTURAL DROUGHT THROUGH LAND SURFACE WATER INDEX (LSWI) AND SOIL WATER BALANCE MODEL

An attempt was made to address the early season agriculture drought, by monitoring the surface soil wetness during 2010 cropping seasons in the states of Andhra Pradesh and Tamil Nadu. Short Wave Infrared (SWIR) based Land Surface Water Index (LSWI) and Soil Water Balance (SWB) model using inputs from remote sensing and ancillary data were used to monitor early season agriculture drought. During the crop season, investigation was made on LSWI characteristics and its response to the rainfall. It was observed that the Rate of Increase (RoI) of LSWI was the highest during the fortnights when the onset of monsoon occurred. The study showed that LSWI is sensitive to the onset of monsoon and initiation of cropping season. The second part of this study attempted to develop a simple book keeping – bucket type – water tight soil water balance model to derive the top 30cm profile soil moisture using climatic, soil and crop parameters as the basic inputs. Soil moisture derived from the model was used to compute the Area Conducive for Sowing (ACS) during the sowing window of the cropping season. The soil moisture was validated spatially and temporally with the ground observed soil moisture values. The ACS was compared with the RoI of LSWI. The results showed that the RoI was high during the sowing window whenever the ACS was greater than 50% of the district area. The observation was consistent in all the districts of the two states. Thus the analysis revealed the potential of LSWI for early season agricultural drought management

Assessment of satellite and model derived long term solar radiation for spatial crop models: A case study using DSSAT in Andhra Pradesh

Crop Simulation models are mathematical representations of the soil plant-atmosphere system that calculate crop growth and yield, as well as the soil and plant water and nutrient balances, as a function of environmental conditions and crop management practices on daily time scale. Crop simulation models require meteorological data as inputs, but data availability and quality are often problematic particularly in spatialising the model for a regional studies. Among these weather variables, daily total solar radiation and air temperature (Tmax and Tmin) have the greatest influence on crop phenology and yield potential. The scarcity of good quality solar radiation data can be a major limitation to the use of crop models. Satellite-sensed weather data have been proposed as an alternative when weather station data are not available. These satellite and modeled based products are global and, in general, contiguous in time and also been shown to be accurate enough to provide reliable solar and meteorological resource data over large regions where surface measurements are sparse or nonexistent. In the present study, an attempt was made to evaluate the satellite and model derived daily solar radiation for simulating groundnut crop growth in the rainfed distrcits of Andhra Pradesh. From our preliminary investigation, we propose that satellite derived daily solar radiation data could be used along with ground observed temperature and rainfall data for regional crop simulation studies where the information on ground observed solar radiation is missing or not available

Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton

GEOSPATIAL ANALYSIS OF NEAR-SURFACE SOIL MOISTURE TIME SERIES DATA OVER INDIAN REGION

The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period

A facile biosynthesis of copper nanoparticles: A micro-structural and antibacterial activity investigation

Nanostructured copper particles are synthesized by Garcinia mangostana leaf extract as reducing agent with copper nitrate. X-ray diffraction study confirms the formation of nanocrystalline cubic phase of copper nanoparticles. The micro-structural properties such as grain size, strain, dislocation density and particle size are examined. The lattice constant is calculated using Nelson–Riley function. Physical parameters like lattice constants, stress, strain, dislocation density and size are calculated. Differential thermal analysis (DTA) and thermo gravimetric (TGA) have confirmed that nanoparticles have phase purity and weight loss percentage is 3.328%. The particle size calculated from XRD is 26.51 nm which is in good agreement with the results of W–H plot, SSP methods and particle analyser. The morphology of prepared copper nanoparticles is characterized by scanning electron microscope (SEM) and TEM. These biologically synthesized nanoparticles are highly antibacterial against Escherichia coli and Staphylococcus aureus