Saveetha University

The design of hybrid electrode materials that synergistically combine high pseudocapacitance and fast charge transport is crucial for the development of next-generation supercapacitors. In this study, a novel Fe-SnO2/ Ti3C2Tx MXene composite was successfully synthesized via a hydrothermal route and investigated for its electrochemical performance in a three-electrode configuration. Structural and morphological analyses using XRD, SEM, TEM, BET and EDS confirmed the uniform anchoring of Fe-SnO2 nanoparticles onto the layered MXene sheets, forming a well-integrated hybrid nanostructure. Electrochemical measurements conducted in 1 M KOH electrolyte demonstrated that the composite exhibits superior charge storage capabilities with a high specific capacitance of 1225.6 F g-1 at 1 A g-1, excellent rate performance with 277.2 F g-1 retained at 30 A g-1, and outstanding cycling stability, maintaining 92.7 % capacitance after 10,000 cycles. The Fe-SnO2/Ti3C2Tx composite electrode outperformed its individual counterparts due to the synergistic interaction between the redoxactive metal oxides and the highly conductive, ion-accessible MXene matrix. This work highlights the potential of such hybrid nanostructures for high-performance supercapacitor applications and provides a framework for future electrode design strategies.

Coffee has long promoted international trade and prosperity, employing millions of small-scale producers. The high demand for this crop has resulted in global supply networks. Young coffee seedlings are vulnerable to fungal diseases such as damping-off and root rot, which cause significant damage and substantially reduce plant productivity. Signs include wilting, root rot, and seedling death both before and after sprouting. Deep learning could allow automatic and scalable prediction of plant diseases. This study aims to enhance early detection of coffee seedling diseases, ensure model adaptability across samples, and optimize computational efficiency for practical implementation. The proposed Vision-based Heterogeneous Graph Neural Network (Vi-HGNN) model, which combines computer vision and graph neural networks (GNNs), provides information about disease transmission patterns over time and space. After training, the model can accurately detect early signs of infection, allowing farmers to intervene before the damage spreads. Experimental results show that Vi-HGNN achieves a 97.77 % detection accuracy, outperforming existing methods in precision, F1-score, and pathogen coverage. Future developments will aim to expand detection capabilities to include additional diseases, pests, and weeds, improving overall crop health monitoring.

This study examines the impact of solution treatment with both short and long-term aging on the Ti-6Al-4V (Grade 5) alloy's mechanical, wear, and corrosion properties. Following a solution treatment that lasted 60 min at 960 °C, the short-term aging (DHTS) treatment for 120 s at 550 °C was conducted. Long-term aging (DHTL) procedures involve aging an alloy for 300 min at 550 °C after the solution treatment. A ball-on-plate sliding wear testing equipment with Ti-6Al-4V alloy as the plate and an alumina ball as a pin was utilized. Open-circuit potential-time measurements and potentiodynamic polarization studies were employed to assess the corrosion potential behaviour in a 3.5

The global administrations are focusing much on the development of sustainable materials, and that too with the help of industrial waste. The inclusion of wastage not only sustains the green environment but also avails the cost-benefit in disposing of the waste. The present research targets the processing of a novel sandwich Kevlar fibre woven resin-impregnated composite reinforced core with by-products of waste tyres called crumb rubbers. The usual open moulding technique was followed to fabricate the sandwich composite with varying weight percentages of crumb rubber as 0, 5, 10 and 15 (KF-CR-0, 5, 10, 15). The structural and thermal rigidity of the proposed composite was examined through tensile testing and thermogravimetric analysis (TGA). The tensile test results indicate the exceptional structural rigidity of KF-CR-15 by possessing 68.30 % higher tensile strength than KF-CR-0. The TGA result corroborates a delayed thermal degradation of the sandwich composite with higher crumb rubber loading. Further, the results reveal that 51 % escalated width of thermal degradation is observed for the sandwich composite with a higher loading of crumb rubber than the zero loading. The fracture morphology executed after the tensile test distinguishes the delamination effect and firm bonding to proclaim a clear understanding of the strengthening mechanism.

The aim of this study is to inspect how dusty Boger hybrid nanofluid is affected by Stefan blowing and elastic deformation across a sheet in the occurrence of gyrotactic microorganisms. The impact of the Cattaneo–Christov flux model is examined in relation to the phenomena of mass and heat. The model predicts a steeper concentration gradient and a slower temperature distribution than Fourier's laws and traditional Fick's, respectively. The occurrence of gyrotactic microbes improves the flow properties. Optimizing the movement and dispersion of microbes is essential for numerous processes, including biofuel production and wastewater treatment, and can improve the design and performance of bioreactors. Environmental engineering can also help from this model, since it helps to explain how pollutants disperse in naturally occurring water bodies. Using a latest kind of non-dimensional variables, the governing PDEs (partial differential equations) are changed into nonlinear ODEs (ordinary differential equations). We subsequent use the RKF-4th-5th method to mathematically resolve the ordinary differential equations. The outcomes show that both the dust and fluid phase temperature and solutal distributions decrease, the flow distributions for both phases grow as the Stephan blowing and elastic deformation parameters augment.