Inorganic Chemistry, General Chemistry, Spectroscopy, Multidisciplinary
4
Scopus Publications
Scopus Publications
Green Synthesis of Copper Nanoparticles, Characterization, Recent Progress and Applications: An Overview V. Adlin Asha, S.R. Pooja, P. Jebha Starling, J. Johnsy Rose, J. Rathidevi, P. Jose Asian Journal of Chemistry, 2025 Sustainable and environmentally friendly nanomaterials are in greater demand, which has sparked interest in green synthesis of metal nanoparticles. In recent years, copper nanoparticles (CuNPs) have emerged as promising materials owing to their outstanding physico-chemical and biological attributes and economic viability. To promote safer and more sustainable synthesis approaches, green synthesis methods have been developed using plant extracts and other biological resources as natural reducing and capping agents. The structural and morphological characteristics of the synthesized CuNPs are generally analyzed with various techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and ultraviolet–visible (UV-Vis) spectroscopy. The wide range of uses of copper nanoparticles is also covered in this review, with special attention paid to their antibacterial, antifungal, antiviral and anticancer activity. Standardizing green synthesis procedures and increasing production while preserving the stability, homogeneity and reproducibility of nanoparticles is a crucial research need, despite the fact that numerous studies have shown. Future studies should also look into long-term environmental effects and synergistic processes in biomedical applications. By encompassing these perspectives, the present review aims to offer an in-depth understanding of green synthesis strategies for CuNPs and their role in fostering sustainable nanotechnological innovations.
Innovative Neural Network Control for Unified Power Quality Conditioner in Photovoltaic Systems Enhancing Grid Stability and Energy Efficiency G.K. Jabash Samuel, Abhinav Pathak, V. Adlin Asha, K. Vijayakumar, M. Nabeela, S. Sivarajan 2025 8th International Conference on Circuit Power and Computing Technologies Iccpct 2025, 2025 The integration of grid faces significant power quality challenges due to increasing use of power electronic devices and the integration of Distributed Energy Resources. It is essential to maintain power quality consistently, regardless of load variations under both steady-state and fault conditions. This paper proposes employing a Unified Power Quality Conditioner (UPQC) system combined with solar Photovoltaic (PV) technology to improve power quality issues and maintain a stable electricity supply. The proposed innovation integrates a single DC link that connects back-to-back voltage-compensating elements configured in both series and shunt, forming PVUPQC system. Primary objective of paper is power quality enhancement of grid-integrated solar PV systems. Novelty of proposed work consists of enhancement of a grid-integrated solar PV system with Artificial Neural Network (ANN) controller-based UPQC. The UPQC enhances power quality in a grid-connected solar PV system by mitigating voltage fluctuations, harmonics, and reactive power imbalance. Presented approach is implemented in MATLAB/Simulink. The simulation results validate that the ANN controller-based UPQC effectively produces sinusoidal grid currents, achieving approximately $\mathbf{1. 6 0} \boldsymbol{\%}$ Total Harmonic Distortion (THD), thereby enhancing efficiency of interconnected PV power distribution network.
Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation Shashi Prakash Dwivedi, Kavitha Dasari, Asha V, Lavish Kansal, Laith H. Alzubaidi, Anurag Kumar Tiwari Proceedings 2024 13th IEEE International Conference on Communication Systems and Network Technologies Csnt 2024, 2024 Medical imaging has changed a lot since “Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple ContrastEnhanced Images and Multi-Map Generation” came out. This innovative technology might revolutionize diagnostic imaging by providing a complete view of tissue features and enhancing accuracy. This study describes a system that utilizes ultrasound, PET, MRI, and CT data. Better detection is the goal of this technology. This first segment begins a new medical imaging age in which clinicians may view the full patient by combining data from diverse sources. The strategy might increase diagnosis accuracy, treatment focus, and healthcare quality. This research uses cutting-edge deep learning, feature extraction, and fusion techniques. DLESA synthesizes images using convolutional neural networks. Since no one does it by hand, errors are less likely. The Feature Extraction and Fusion Algorithm (FEFA) integrates significant features from several picture approaches to maximize their benefits. CERA and SAEA are essential for picture clarity and line accuracy. Reality reveals that the proposed way performs better than usual. PSNR (13.29%), SSIM (6.98%), CNR (11.61%), and Dice Coefficient (8.54%) have all increased. The technology may be utilized outside of healthcare since it employs science to mix data, learn deep, and extract characteristics. This strategy that integrates multiple disciplines of research might revolutionize how we obtain, analyze, and use information, leading to groundbreaking scientific, technological, and health advances. This novel technology advances diagnostic pictures and data-driven diagnostics, bringing accuracy and comprehension to medical and other disciplines.
