Carbon Nanotube from Unconventional Precursor-Optimization of Synthesis Parameters

Carbon is a versatile element of distinctive properties and has been described as the key element of living substance. Carbon nanostructures have attracted lots of interest, due their prominent properties. Spray pyrolysis method is adopted for synthesis of carbon nanotubes (CNTs). Contrast to any petroleum product, there is no fear of its ultimate shortage as it is a renewable source and can be obtained easily by cultivating as much quantity as required. Synthesize well crystalline multiwalled carbon nanotubes (MWNTs) from unconventional precursor of methyl ester of Helianthus annuus oil by optimize the parameters such as reaction temperature, catalyst composition and feed rate of carbon precursor in order to obtain good yield with desirable morphology

House Price Prediction using Machine Learning Algorithms

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset

Efficient Electrolytes for Lithium–Sulfur Batteries

This review article mainly encompasses on the state-of-the-art electrolytes for lithium–sulfur batteries. Different strategies have been employed to address the issues of lithium–sulfur batteries across the world. One among them is identification of electrolytes and optimization of their properties for the applications in lithium–sulfur batteries. The electrolytes for lithium–sulfur batteries are broadly classified as (i) non-aqueous liquid electrolytes, (ii) ionic liquids, (iii) solid polymer, and (iv) glass-ceramic electrolytes. This article presents the properties, advantages, and limitations of each type of electrolytes. Also, the importance of electrolyte additives on the electrochemical performance of Li–S cells is discussed

COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things

Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models

A Co9S8 microsphere and N-doped carbon nanotube composite host material for lithium-sulfur batteries

Lithium-sulfur batteries have emerged as extraordinarily favorable energy storage devices due to their high specific capacity and energy density, safety and low cost. Unfortunately, the wide applications of lithium-sulfur batteries are hampered by several issues, such as the low electronic conductivity and slow redox kinetics, serious volumetric expansion and polysulfide “shuttle effect”. To overcome these issues, in our work, we design and synthesize a composite sulfur host material of Co9S8 microspheres and N-doped carbon nanotubes, where the metallic sulfide Co9S8 with a good conductivity enables the immobilization of the polar lithium polysulfides owing to the strong polar chemisorptive capability, and the one dimensional N-doped carbon nanotubes can provide channels for fast electron and lithium-ion transport. As the lithium polysulfides are well confined, and the redox conversions are promoted, the Co9S8@N-CNTs/S-based lithium-sulfur battery possesses a superior energy storage performance, exhibiting a large specific capacity of 1233 mAh g-1 at 0.1 C and an outstanding cyclic performance, with a low decay of 0.045% per cycle and a Coulombic efficiency of more than 99% after 1000 cycles

AUTOMATIC BRAIN TUMOUR SEGMENTATION OF MAGNETIC RESONANCE IMAGES (MRI) BASED ON REGION OF INTEREST (ROI)

Segmentation is one of techniques used for classifying brain tissues in Magnetic Resonance Image (MRI) for identifying anatomical structures in the brain. The automated brain tumour segmentation remains challenging and computationally intensive because tumour appears in different size and intensity. In this paper, we have proposed a method for fast and automatic segmentation of tumour from Region of Interest (ROI) identified in MRI. ROI is a smaller portion of the image containing tumour. In the first step, tumour slices are identified using bilateral asymmetry property of the brain. In the second step, the ROI is identified using quadtree decomposition and similarity detection based on coefficient computed with gray level intensity histograms. In the third step, only the ROI is segmented using spectral clustering method rather than considering the whole image. Experimental results on real-world datasets are carried and compared with the recent existing works which show better results in terms of accuracy and less processing time for segmentatio

Ionic conductivity and interfacial properties of nanochitin-incorporated polyethylene oxide–LiN(C2F5SO2)2 polymer electrolytes

Nanocomposite polymer electrolytes (NCPE) composed of poly(ethylene oxide) and nanochitin for different concentrations of LiN(C2F5SO2)2 (LiBETI) were prepared by a completely dry, solvent-free procedure using a hot press. The thermal stability of NCPE membranes was investigated by DSC and TG-DTA. The membraneswere subjected to SEM, ionic conductivity and FTIR analysis. Li/NCPE/Li symmetric cellswere assembled and the variation of interfacial resistance as a function of time was also measured. The surface chemistry of lithium electrodes in contact with NCPE revealed the formation of Li–O–C and LiN compounds. LiFePO4/NCPE/Li cell was assembled and the cycling profile showed a well-defined and reproducible shape of the voltage curves thus indicating a good cycling behavior of the cell at 60 ◦C

Composite Polymer Electrolytes Encompassing Metal Organic Frame Works: A New Strategy for All-Solid-State Lithium Batteries

Magnesium-benzene tricarboxylate metal organic framework (Mg-BTC MOF)-incorporated composite polymer electrolytes (CPE) composed of poly(ethylene oxide) (PEO) and lithium bistrifluoromethane sulfonylimide (LiTFSI) were prepared by a simple hot-press technique. The incorporation of Mg-BTC MOF in the polymeric matrix has significantly enhanced the ionic conductivity of CPE up to two orders magnitudes even at 0 °C. It also improved the thermal stability, compatibility, and elongation-at-break of the polymeric membrane. The all-solid-state lithium polymer cell composed of Li/ CPE/LiFePO4 has delivered a stable discharge capacity of 110 mAh g−1 at 70 °C with a current rate of 1-C, which is higher than that of those reported earlier. The appealing properties such as high ionic conductivity, better compatibility, and stable cycling qualify this membrane as electrolyte for all-solid-state lithium batteries for elevated temperature application