Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Expanded graphite (EG), a derivative of graphite with a unique worm-like porous structure, has become popular owing to its exceptional thermal, electrical, and chemical properties. EG is combined with phase-change materials (PCM) to form a composite PCM with enhanced properties, and EG serves as a functional additive. This review explores the preparation of EG, along with a detailed study of its properties and characterization. Various preparation methods for EG, including thermal, chemical, and electrochemical processes, are elaborately explained with detailed analyses of the temperature, intercalating agent, and reaction time to achieve the final material property. This review further examines the role of EG in combination with PCM for energy storage systems and building applications, highlighting the advantages and disadvantages of using EG in these fields. The current challenges faced by the addition of EG, including optimization and large-scale production, are briefly discussed. Finally, future research directions for EG are proposed, explaining the setup of the current technology. The review concludes with the importance of EG in guiding the development of new materials for the next generation, considering the balance between the performance and cost of the material.

Multiphase compounds exhibiting a prominent orthorhombic V2O5 structure have been developed using the empirical formula V2−xNi2xSbxO5−δ (0.05 ≤ x ≤ 0.08). Microstructure and surface chemistry analyses confirm that the incorporation of Ni and Sb in the V2O5 matrix results in the formation of new phases corresponding to the foreign cations. Raman analysis shows the excitation of 12 active phonon modes with Ґ-Raman = 6Ag + 2B1g + 2B2g +1B3g + 1, with 11 vibrational modes specifically characterizing the α-polymorphic form of orthorhombic V2O5. The photoluminescence spectrum near-band edge blue-green emissions is accompanied by broad peaks at 541 nm and 564 nm. The decrease in PL intensity with the increasing phase composition of SbVO4 and NiV2O6 indicates improved separation of photogenerated charge carriers, which suppresses electron–hole recombination and facilitates the generation of reactive oxygen species. The synthesized materials exhibit strong, broad-spectrum antimicrobial activity against both fungal and bacterial strains. The inhibition zone diameters increase with molar fraction (x), reaching maximum values for Aspergillus niger (25 mm), Penicillium sp. (19 mm), Staphylococcus aureus (18 mm), Pseudomonas aeruginosa (20 mm), and Escherichia coli (15 mm).

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

In the present study, PLA/β-TCP bio-composite filaments, at different proportion of β-TCP of 5, 10, and 15 wt

Catastrophic forgetting was a phenomenon in artificial neural networks where a model rapidly and severely loses its performance on previous learned tasks. It happens because the network’s weights are updated to meet the new task’s objectives, which causes loss of information in previous tasks. Autonomous driving systems rely on the integration of high-performance computing, deep learning algorithms, and advanced sensors like LiDAR, cameras, and radar to process real-time environmental data and make instantaneous driving decisions. Various research works are conducted to develop autonomous vehicle image classification systems, but still numerous challenges were rectified. To address these challenges an innovative model is implemented in current autonomous vehicle classification. Initially, necessary images were acquired from standard dataset. These images were subsequently enhanced using Vision Transformer-Retinex (ViT-Retinex) to effectively manage diverse weather and light conditions. The pre-processed images undergo object detection using the Recurrent MobileNet Single Shot MultiBox Detector (RM-SSD), which is designed for consuming limited computational power. After detecting the objects, Incremental learning using the Adaptive Pyramid Dilated Mobilenetv3 Classifier (IL-APDMV3) is implemented to classify objects and mitigate catastrophic forgetting where new data acquisition compromises the retention of previous knowledge. The weights of the IL-APDMV3 model are optimally selected by Enhanced Random Variable-based Fossa Optimization Algorithm (ERV-FOA) for fine-tuning system’s superior object recognition and classification performance. Finally, performance of suggested method is validated against prior methods to prove effectiveness of system. This developed work has achieved 98.16