Synthesis and Characterization of Poly Styrene-Co-Poly 2-Hydroxyethylmethacrylate (HEMA) Copolymer and an Investigation of Free-Radical Copolymerization Propagation Kinetics by Solvent Effects

A series of homo and copolymers of styrene (ST) and 2-hydroxyethyl methacrylate (HEMA) in three different media (bulk, tetrahydrofuran, and benzene) have been investigated by free radical polymerization method. The samples obtained from the synthesis were characterized by Fourier Transform-Infrared spectroscopy (FT-IR), proton nuclear magnetic resonance spectroscopy (1H NMR), atomic force microscopy (AFM), and differential scanning calorimetry (DSC). The results show that the synthesis of the polymers is more feasible under neat conditions rather than solvent directed reaction. Moreover, the DSC data shows that the polystyrene obtained is amorphous in nature and therefore displayed only a glass transition signal rather than crystallization and melting peaks. In addition, this study indicates that homolopolymerization of styrene via free radical polymerization tends to be preferable in less polar solvents like THF than in non-polar solvents like benzene. Benzene might destabilize the formation of the reactive radicals leading to the formation of the products. In summary, the homolpolymerization of styrene is more feasible than the homopolymerization 2-hydroxyethyl methacrylate under the experimental setup used. Styrene is more reactive than 2-hydroxyethyl methacrylate than free radical polymerization reaction due in part of the generation of the benzylic radical intermediate which is more stable leading to the formation of products than alkyl radical which are less stable. Furthermore, polymerization of styrene under neat conditions is preferable in solvent-assisted environments. The choice of solvent for the synthesis of these polymers is crucial and therefore the selection of solvent that leads to the formation of a more stable reaction intermediate is more favorable. It is worth noting that the structure of the proposed copolymer consists of a highly polar and hydrophilic monomer, 2-hydroxyethyl methacrylate and a highly non-polar and hydrophobic monomer, styrene. These functionalities constitute an amphiphilic copolymer with diverse characteristics. A plausible explanation underlying our observations is that the reaction conditions employed in the synthesis of these copolymers might not be the right route required under free radical polymerization.

X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.