Author: "Karuna, J." - Searchworks@Jio Institute Digital Library Search Results

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Author: Vijay, V., Karuna, J., Archana, J., and Navaneethan, M.
Subjects: *SEEBECK coefficient, *HYBRID materials, *THERMOELECTRIC materials, *CARRIER density, *CARBON composites, *PLASMA chemistry
Abstract: Higher manganese (TE) silicide (HMS), an eco-friendly, low-cost, and earth-abundant p-type thermoelectric material, exposes a Nowotny chimney-ladder crystal structure, obeying the 14th-electron rule. Here, a hybrid composite of a carbon nanofiber (CNF) and HMS was synthesized by vacuum melting followed by spark plasma sintering, and its thermoelectric performance were demonstrated. The incorporation of CNF with HMS notably enhances the Seebeck coefficient and decline the thermal conductivity without significantly affecting the carrier concentration and electrical conductivity due to the interfacial energy filtering effect. A maximum Seebeck coefficient of 307 μV/K was recorded for the 0.5% CNF composite with HMS, leading to accomplish a high-power factor of 1755.63 μW/mK2 at 803 K. Also, interfaces, grain boundaries, and dislocations leading to the high magnitude of strain, confirmed from HR-TEM and strain analysis, leads to decrease in lattice thermal conductivity to 1.95 W/mK. As a result, HMS attains the peak zT value of 0.64, suggesting that carbon-based composites are a promising way to boost the thermoelectric performance of HMS-based compounds. [ABSTRACT FROM AUTHOR]
Published: 2024
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Author: Manohar N and Karuna J Gowda
Subjects: Identification (information), biology, Smut, Feature extraction, Blight, Paddy field, Segmentation, Image processing, Image segmentation, Agricultural engineering, biology.organism_classification, Mathematics
Abstract: Paddy is the main nourishment in the south sections of India. It straightforwardly influences the country's infrastructure. Bacterial leaf blight, brown spot and leaf smut were its main illnesses found in paddy field crops, which also significantly affect its profitability. This work presents an image processing system based automatic identification and classification of various paddy leaf diseases by affecting the cultivation of paddy namely bacterial leaf blight, brown spot, and leaf smut. The key phases of proposed methodology are pre-processing of images, segmentation, feature extraction and classification. The technique used in the proposed work to identify paddy leaf disease involves Otsu threshold method for segmentation, grey level co-occurrence metrics for feature extraction and k-nearest neighbors algorithm for classification. The proposed system aims at achieving rapid and accurate identification of disease and classification of the disease type and also helps in classifying the various paddy leaf diseases by utilizing the texture and shape features that majorly contributes in leaf disease recognition.
Published: 2020
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