Velammal Institute of Technology

Poly(3,4-ethylenedioxythiophene)–polyindole (PEDOT–PIn) is a distinguished π-conjugated conducting polymer; however, its inherent brittleness and loosely arranged molecular framework hinder its long-term operational reliability in practical devices. To address these shortcomings, a novel niobium nitride (NbN)–PEDOT–PIn hybrid nanocomposite is synthesized, featuring a mechanically stable three-dimensional network formed by embedding PEDOT–PIn nanostructures within stratified layers of NbN through a controlled oxidative polymerization strategy. This multifunctional material functions both as a high-efficiency electrode for electrochemical energy storage and as an effective catalyst for electrochemical and photocatalytic applications. The hybrid material exhibits outstanding electro catalytic sensitivity toward Fenbendazole (FNB), as assessed by differential pulse voltammetry (DPV). As an energy storage medium, the NbN–PEDOT–PIn composite enables rapid and reversible ion storage processes, as demonstrated by galvanostatic charge–discharge (GCD) testing, where the hybrid electrode delivers a high specific capacitance(SC) of 680 F g−1 at a current density of 1 A g−1. Cyclic voltammetry (CV) and log–log plot analysis confirms a capacitive contribution exceeding 97

The untreated natural fiber reinforced polymer composites limited their broader structural applications due to their limited mechanical performance and poor interfacial adhesion. To counteract this, the tensile strength of modified banana fiber reinforced epoxy composites incorporated with nano-silica (SiO2) at different concentrations of 0.5 to 3 wt

This study presents a novel approach to polymer composite design by hybridizing rigid natural fibers with elastomeric and bio-based fillers to achieve multifunctional performance. The research demonstrates that strategic reinforcement tailoring can significantly enhance toughness, tribological resistance, and thermal stability (HDT). Mechanical and thermal evaluations revealed distinct behaviours depending on the hybrid reinforcement combinations. In drop-load impact tests, specimens M3 and M4 exhibited the highest peak forces (1450–1550 N) and energy absorption (12 J), indicating superior impact resistance. Wear analysis showed that M4 achieved the lowest coefficient of friction (0.30) and specific wear rate (2.0 × 10–6 mm3/N-m), while M6 outperformed sisal-only M5 due to rubber-assisted crack suppression. Heat deflection temperature (HDT) measurements confirmed strong thermal stability for M2 (71.4 °C), M3 (70 °C), M4 (71 °C), and M6 (69.4 °C), whereas M1 (42.8 °C) and M5 (45.9 °C) softened earlier. Overall, M4 demonstrated the most balanced combination of impact strength, wear resistance, and thermal stability, establishing the effectiveness of hybrid reinforcement strategies in multifunctional polymer composites.

This research aims to explore the mechanical and microstructural properties of AZ91 magnesium alloy, which is reinforced with ZrC nanoparticles and produced through stir casting enhanced by ultrasonic assistance. The material’s inherent limitations in overall mechanical performance and toughness under harsh conditions are meant to be solved by the addition of ZrC nanoparticles. Tensile strength, hardness, and varied ZrC concentrations (1.5

A stroke, often known as a brain attack, results from a cut off blood supply to the brain or from anything blocking the blood vessels. In any of these conditions, damage to or death in the brain occurs. Every ability in our body—including memory, breathing, hormone production and release, and everything—is under the control of our brain. Lack of oxygen causes cells in the brain to die in a second should the blood supply to the brain get blocked. This causes strokes at last. Stroke is one of the most frequent reasons for death globally. According to the World Health Organization (WHO), stroke is responsible for 11% of worldwide mortality. Therefore, based on medical data inputs, including health risks that may contribute to strokes, such as smoking, cardiovascular disease, obesity, high blood cholesterol, insulin levels, and elevated blood pressure, we propose a machine learning (ML) model with supervised learning methodologies that can predict whether or not a person is likely to have a stroke. This study compares many ML approaches, including the Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), as well as Gradient Boosting (GB). The model we developed results show that the suggested approach significantly increases system efficiency (97.62%) and accuracy.