The role of metal oxides on the thermal decomposition of poly(vinyl chloride) (PVC) and poly-(vinyl acetate) (PVAC) and their blends was investigated by thermogravimetry (TGA). While the degradation of PVAC was mildly affected by the presence of metal oxides, the degradation of PVC was greatly influenced by metal oxides. Both polymers followed a two-step degradation mechanism involving chlorine or acetate radical removal followed by polyolefinic backbone breakage. The isokinetic temperatures and rate determined from the compensation plots indicated that the mode of olefinic backbone breakage is the same for both the polymers. FTIR studies after the first stage showed the disappearance of the C-Cl of PVC and CdO and C-O groups of PVAC, suggesting the formation of a polyolefinic chain. Blends of PVC-PVAC were obtained by solution blending by dissolving the polymers in tetrahydrofuran. Scanning electron microscopy and TGA showed complete miscibility of polymers in the blend. The first-stage degradation of the blend was greatly influenced by the presence of PVC and metal oxides, suggesting that hydrogen chloride liberated from PVC influenced the decomposition behavior of PVAC. The second-stage degradation (olefinic breakage) of the blends was mildly affected by the metal oxides and the breakage was similar to that of pure polymers.
COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN’s prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.
Three p-electron rich fluorescent supramolecular polymers (1-3) have been synthesized incorporating 2-methyl-3-butyn-2-ol groups in reasonable yield by employing Sonagashira coupling. They were characterized by multinuclear NMR ( 1 H, 13 C), ESI-MS and single crystal X-ray diffraction analyses [1 = 1-(2-methyl-3-butyn-2-ol)pyrene; 2 = 9,10-bis(2-methyl-3-butyn-2-ol)anthracene; 3 = 1,3,6,8-tetrakis(2methyl-3-butyn-2-ol)pyrene]. Single crystal structures of 1-3 indicated that the incorporation of hydroxy (-OH) groups on the peripheral of the fluorophores helps them to self-associate into an infinite supramolecular polymeric network via intermolecular hydrogen bonding interactions between the adjacent discrete fluorophore units. All these compounds showed fluorescence characteristics in chloroform solution due to the extended p-conjugation and were used as selective fluorescent sensors for the detection of electron deficient nitroaromatics. The changes in photophysical properties of fluorophores (1-3) upon complex formation with electron deficient nitroaromatic explosives were studied in chloroform solution by using fluorescence spectroscopy. All these fluorophores showed the largest quenching response with moderate selectivity for nitroaromatics over various other electron deficient/rich aromatic compounds tested (Chart 1). Analysis of the fluorescence titration profile of 9,10-bis(2-methyl-3butyn-2-ol)anthracene fluorophore (2) with 1,3,5-trinitrotoluene/2,4-dinitrotoluene provided evidence that this particular fluorophore detects nitroaromatics in the nanomolar range [2.0 ppb for TNT, 13.7 ppb for DNT]. Moreover, sharp visual color change was observed upon mixing nitroaromatic (DNT) with fluorophores (1-3) both in solution as well as in solid phase. Furthermore, the vapor-phase sensing study of thin film of fluorophores (1-3) showed efficient quenching responses for DNT and this sensing process is reproducible. Selective fluorescence quenching response including a sharp visual color change for nitroaromatics make these tested fluorophores (1-3) as potential sensors for nitroaromatic compounds with a detection limit of ppb level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.