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The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices.

A water stable zinc-MOF (ZnGlu) catalyst was facilely prepared from the proteinogenic amino acid, l-glutamic acid at room temperature in aqueous medium. CO2 fixations were promoted by the ZnGlu catalyst's inherently coordinated water and externally added water in yielding cyclic carbonate and cyclic urethane at room temperature. This eliminates the need for catalyst activation, making ZnGlu a ready-to-use catalyst. The enhanced CO2 cycloaddition with added water hints at the application of ZnGlu in wet flue gas conversions. This is the first reported attempt for the use of an MOF in the cycloaddition of aziridine and CO2.

Reported is the application of ZIF-90, which is a highly porous zeolitic imidazolate framework, as a novel catalyst for the cycloaddition of propylene oxide (PO) with CO 2 in the absence of co-catalysts and solvents under moderate reaction conditions. The effects of various reaction parameters were investigated. The activity of ZIF-90 was compared with that of various metal-organic-framework (MOF)-based catalysts for the cycloaddition of PO with CO 2 . Density functional theory calculations elucidated the role of ZIF-90 in creating a favorable environment for the PO-CO 2 cycloaddition reaction. A reaction mechanism for the ZIF-90-catalyzed PO-CO 2 cycloaddition on the basis of DFT calculations is proposed and the regeneration of ZIF-90 is discussed., (Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A novel application of alkanolamines, widely employed as CO2 scrubbers in catalyzing the insertion of CO2 into epoxides generating cyclic carbonates in excellent yield and selectivity via the synergistic activity of hydroxyl and amine groups, is unravelled along with computational studies.

Naturally occurring amino acids were identified as efficient co-catalysts for the alkali metal halide-mediated synthesis of cyclic carbonates from carbon dioxide and epoxides under mild, solvent free reaction conditions. The binary system of histidine/potassium iodide gave an appreciable turnover number of 535 for propylene oxide in 3 h. Detailed studies evaluating a variety of amino acids revealed that the basic amino acids afforded better conversion rates. The formation of a seven membered ring involving the zwitterionic ends of the amino acid, the metal halide, and the epoxide was considered to accelerate the catalysis rate. Density functional theory calculations were performed for the first time on amino acid co-catalyzed cycloaddition to provide further evidence for this hypothesis. The iodide ions of the alkali metal halide displayed excellent synergism with the hydrogen bonding groups of the amino acids in the production of cyclic carbonates, whereas bromide and chloride anions functioned less efficiently. The utilization of amino acids to enhance the catalytic activity of the cheap and eco-friendly alkali metal halides for cyclic carbonate synthesis represents a cost-effective, greener route towards the chemical fixation of carbon dioxide.

Microwave-assisted quaternization of glycine (GLY) resulted in the synthesis of an efficient catalyst, quaternized glycine (QGLY), for the solventless synthesis of cyclic carbonates from epoxides and CO2 under mild reaction conditions. Density functional theory was used to simulate the synergistic influence of the COOH group and the halide ion of QGLY in enhancing the catalytic activity.