Remarkable photocatalytic performances towards pollutant degradation under sunlight and enhanced electrochemical properties of TiO2/polymer nanohybrids

In this work, TiO₂-based nanocomposites containing polyaniline (PANI), poly(1-naphthylamine) (PNA), and polyindole (PIN) were synthesized by effective and simple routes and posteriorly employed as photocatalysts and supercapacitors. Characterization techniques such as XRD, FTIR, FESEM, UV, and PL were employed to investigate the structural, morphological, and optical properties of materials. XRD analysis confirmed the successful formation of TiO₂ and TiO₂/polymer nanocomposites. PANI, PNA, and PIN polymers were well distributed on the surface of TiO₂ nanoparticles and were investigated/explored from the FESEM analysis. The visible light absorption and the recombination rate of photogenerated charge carriers were confirmed by the UV-Vis and PL analysis. The photocatalytic properties of the nanocomposites were investigated towards malachite green (MG) dye degradation under sunlight. The dye degradation efficiency followed the order TiO₂/PNA > TiO₂/PANI > TiO₂ > TiO₂/PIN. The higher efficiency of TiO₂/PNA can be associated with its smaller bandgap energy compared to the other materials. Electrochemical properties of materials were also examined by cyclic voltammetry and galvanostatic charge-discharge measurements using a three-electrode experiment setup in an aqueous electrolyte. TiO₂/PNA nanocomposite showed higher supercapacitor behavior compared to the other materials due to higher electrical conductivity of PNA and redox potential of TiO₂ (pseudocapacitance).

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Mystery inside the tooth: the dental pulp stem cells

Stem cells are distinguished by their ability to differentiate into different types of cells in the body and to self-replicate. During the recent years, stem cells have been used extensively in the field of medicine for the repair and regeneration of defective tissues and organs. However, the knowledge on the stem cell technology is increasing quickly in all medical disciplines and it dictates the need for new protective approaches in all fields, which include reparative dentistry. Stem cell therapy constitutes a common challenge for dentists as well as for biotechnologists. The aim of this study was to review the knowledge which was related to stem cells and to consider the possibility of use of stem cell populations and their technology in the future clinical applications, to cure diseases like Parkinsonism, Juvenile diabetes, certain forms of cancer, spinal injuries and heart problems.