Sri Sivasubramaniya Nadar College of Engineering (SSN), popularly known as SSN College of Engineering or simply SSN, is a private engineering college located in Chennai, Tamil Nadu, India. It is an autonomous college affiliated with Anna University founded by Shiv Nadar. The college is certified to ISO 9001:2000 standard by the National Board of Accreditation.In March 2018, the college was granted autonomous status by UGC.
In recent years, nanotechnology has gained recognition as a promising field for developing nanoscale materials with enhanced functional properties. This novel study presents an economical and eco-friendly method for synthesizing magnesium-copper oxide nanoparticles (Mg–CuONPs) using Argemone mexicana leaf extract. The formation of Mg–CuONPs was confirmed by UV–Visible spectroscopy, and Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed the presence of functional groups from phytochemicals that served as both reducing and stabilizing agents, and X-ray Diffraction (XRD) analysis confirmed the crystalline structure. High-Resolution Transmission Electron Microscopy (HR-TEM) analysis affirmed the spherical morphology with an average particle size of 23.98 nm, and zeta potential analysis revealed a negative surface charge (– 10 mV). The biosynthesized Mg–CuONPs displayed the highest significant antioxidant activity of ABTS (2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) of 80.93 ± 1.27
Recent research has emphasized the potential of combining different electrode materials to enhance the performance of asymmetric supercapacitors for energy storage applications. In this study, an asymmetric supercapacitor was developed using cobalt sulfide (CoS)-embedded activated carbon (AC), denoted as CoS@AC, as the positive electrode, while AC alone served as the negative electrode. Initially, micro-flower morphology of CoS was synthesized via a hydrothermal method, and layered morphology of AC was prepared through the carbonization of Acorus calamus. Then CoS@AC nanocomposite was fabricated using a wet impregnation method and its structural, morphological analysis was carried out. The morphological analysis of CoS@AC nanocomposites confirmed the presence of both micro-flower morphology of CoS and layered morphology of AC structures. The TEM analysis of CoS@AC nanocomposite revealed the presence of both micro-flower-like (CoS) and layered-like structure (AC), and the HRTEM analysis showed an interplanar spacing of 0.236 nm related to CoS (101) XRD diffraction. The BET analysis of CoS@AC nanocomposites shows a nearly type-I isotherm with a surface area of about 1145 m2/g, an average pore size of about 4.23 nm, and a pore volume of 0.451 cc/g. Finally, the CoS@AC‖AC electrode demonstrated enhanced electrochemical performance, achieving a specific capacitance of approximately 234 F/g, an energy density of 83.2 Wh/kg, and a power density of 16,089 W/kg. Further, the stability analysis was carried out for 2000 cycles, which showed better stability performance. These results strongly recommend that the CoS@AC‖AC system is favorable for asymmetric supercapacitor applications.
In this work, a MnO2@NiO nanocomposite was synthesized through a hydrothermal approach for non-enzymatic glucose sensing applications. The material structure and its various (hkl) planes were confirmed by the powder X-ray diffraction (PXRD). The plate-like nanosheet’s morphology and their elemental compositions were examined by the field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray spectroscopy (EDX). The electrochemical properties of the prepared materials were investigated by cyclic voltammetry (CV) and amperometry. Based on their results, the prepared modified electrodes demonstrate excellent electrochemical performance for non-enzymatic glucose detection. The MnO2@NiO nanocomposite exhibits good electrochemical activity, and it has a high glucose detection sensitivity of about 723 µA mM−1 cm−2 and a detection limit of 0.55 mM with a wide linear range of 0.25–5.5 mM. Moreover, the MnO2@NiO modified electrode demonstrates excellent repeatability, reproducibility, cyclic stability, and selectivity. It retained a high percentage of its initial current even after 15 days. Furthermore, the anti-interference performance of the prepared MnO2@NiO modified electrode was investigated in the presence of common interfering species like sodium chloride (NaCl), uric acid (UA), potassium chloride (KCL), citric acid (CA), fructose, maltose, and ascorbic acid (AA). These results confirm that the MnO2@NiO modified electrode is an efficient and reliable candidate for non-enzymatic glucose sensing applications.
The process of classifying brain tumors through MRI scans faces difficulties because tumors have different growth patterns and there are not enough diverse datasets and researchers need to create dependable ways to represent important features. This work presents a multiclass brain tumor classification method which uses a hybrid multi-transformer feature fusion framework and Grey Wolf Optimizer (GWO) technology to control hyperparameter optimization. A unified 3584-dimensional representation is created through the extraction of deep features from four pretrained architectures which include Vision Transformer (ViT-B/16) and Swin Transformer (Swin-B) and BEiT (BEiT-B/16) and ConvNeXt (ConvNeXt-B). The GWO optimization with ResNet101 classifier enhances its ability to generalize while reducing the risk of overfitting. The work utilized a Kaggle brain MRI dataset which includes 6799 T1-weighted contrast-enhanced images that show four distinct categories of glioma meningioma pituitary tumor and no tumor. The proposed fusion-based model achieved a test accuracy of 97.73
Due to increasing sustainability concerns, wood filler-reinforced polymer (WFRP) composite materials have gained prominence as a potential material in various industries such as construction, automotive and consumer products. This real-world application of wood filler-reinforced polymer composites requires well understanding and prediction of important mechanical properties. The conventional experimental methods of material characterization are often resource intensive and time-consuming. Recently, the machine learning (ML) presented a novel and viable avenues for augmenting prediction models, enabling the accurate estimation of mechanical properties with fewer experiments and improved generalization. The current work presents the application of ML techniques for the prediction of tensile properties of WFRP composite. Various models like support vector machine, polynomial regression, and decision trees (DT) are explored for their potential to predict tensile properties based on input variables like filler content, and crosshead speed. These models are very effective and accurate in representing highly intricate and nonlinear interdependencies between material input parameters and its performance. Additionally, the artificial neural network model, in particular, exhibits an excellent capability of predicting tensile strength of composites with the lowest MSE value. The study highlights the efficiency of ML models, demonstrating their potential to enhance material property prediction.
