Many plant systems accumulate silica in solid form, creating intracellular or extracellular silica bodies, the so-called phytoliths, which are essential for growth, mechanical strength, rigidity, predator defence and leaf stiffness. Silica is an inorganic amorphous oxide formed by polymerization processes within plants. There has been much research in order to gain new insights into the biochemical processes and to mimic biosilicification. The nanotechnology potential of using plant silica bodies has been realized by several researchers for developing biomimetic devices and in the making of new bionanofunctional materials. In parallel to the rapid rise of the idea of growing  nanotechnology by using diatoms, we have examined and synthesized  information on plant slilica bodies, plant silica uptake mechanisms,  and its bio-nano technological applications and the possible ways of producing biogenic silica bodies with new functions.

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Spoilage of bulk-stored grain leads to decreased nutritional value and poses health hazards due to the formation of irritating volatile metabolites inside grain bins. Co-occurrence of insect species and molds are common in a stored-grain ecosystem and are the causes of spoilage. In this study, new chemical signatures and volatile compounds evolving from grain having a combination of molds and insects were identified. The results of this research provided confident information for the development of odor sensor arrays for grain quality monitoring. In this project, Gas Chromatography and Mass Spectrometric (GC-MS) analysis of the gas samples trapped in dimethylpoly-siloxane was performed. The analytical data from the GC-MS spectra were validated, interpreted and compared using the NIST/NIH (National Institute of Standards and Technology and National Institute of Health, USA) Mass Spectral Library.

software x raySprouted wheat kernels adversely affect bread and pasta making quality, thus lowering the grade and value to millers, bakers and grain dealers. In this study, the potential of using soft X-ray system in detecting the sprouted wheat kernels was evaluated. Sprouted kernels were produced by germinating seeds. Both the sprouted and healthy samples were X-rayed using a soft X-ray system. White specks were observed in all the sprouted kernel X-ray images. Algorithms were written to extract 55 image features including gray level modeling and histogram from the scanned images. Identification of sprouted and healthy kernels was determined using statistical and neural network classifiers. A four-layer back propagation neural network model correctly classified 90% and 95% of the sprouted and healthy kernels, respectively. Statistical classifier correctly identified 87% and 92% of the sprouted and healthy kernels, respectively.

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For high temperatures, corrosive and harsh environmental applications in the agricultural and food industry, SiCN based sensors are preferred. Experimental methods for making thin films of SiCN to facilitate sensor fabrication is explored in this study. Silicon carbonitride films were grown on silicon substrate using ammonia and hexamethyldisilazane gas sources using catalytic chemical vapour deposition process. Parameter regimes such as influence of flow rates of target gas and variation in substrate temperature are identified for effective deposition of SiCN thin films. Compositions of silicon, carbon and nitrogen in the SiCN films were varied by changing the flow rate of ammonia gas. The effect of deposition conditions on the structural, optical and mechanical properties of SiCN thin films was examined. X-ray photoelectron spectroscopy analysis indicated that the higher flow rate of ammonia gas results in higher nitrogen and lower carbon content in the deposited thin films. The measurement of stress as a function of substrate temperature in the SiCN film showed that the stress changes from compressive to tensile in the range of 275°C to 325°C.

grain moisture contentPhysical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology, which in turn affects the grade of the harvested crop. In this project, we evaluated the effect of moisture content on the classification capability of color, morphology and textural features of imaged grains. Color images of individual kernels and bulk samples of wheat and barley were acquired using a high resolution color camera with an IEEE 1394 interface machine vision system. Algorithms developed by Grain Storage Research Laboratory were implemented to extract color, textural and morphological features from grain images. Image segmentation was applied to remove the background from the single kernel images. The extracted features were analyzed and classified for the effect of moisture content using statistical classifiers and a devised back propagation neural network model.

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