NiO/rGO nanocomposites have been synthesized by the hydrothermal method. The various amounts of prepared NiO/rGO nanocomposite were incorporated with perovskite material for fabricating the perovskite solar cells (PSCs). The structure of NiO/rGO-CH3NH3PbI3 based PSC is FTO/c-TiO2/m-TiO2/NiO/rGO-CH3NH3PbI3/Carbon. The present NiO/rGO-CH3NH3PbI3 based PSC obtained a power conversion efficiency (PCE) of 15.27
In recent years, wearable antennas gain significant attention for use in healthcare applications, particularly in Implantable Medical Devices (IMDs) and Medical Body Area Networks. These antennas must be small, lightweight, and body-conforming to meet the needs of these applications. As the wearable antennas play a crucial role in Medical Body Area Networks (MBAN), facilitating continuous health monitoring. Their integration in IMDs requires minimizing electromagnetic interference and optimizing radiation characteristics. As healthcare systems demand more efficient and precise devices, the development of wearable antennas becomes essential for enhanced performance. The development of such antenna has gathered substantial awareness in recent years in the telemedicine industry. This study proposes a two-dimensional square loop-based antenna design, operating at 2.4 GHz with improved radiation properties and reduced backward radiation. The antenna dimensions are 60 × 40 × 0.7 mm3, making it compact and effective for body-worn applications. The antenna design process incorporates Machine Learning (ML) techniques to minimize simulation time, increase efficiency, and enhance design accuracy. ML algorithms optimize the antenna’s performance, particularly in terms of reflection coefficient, bandwidth, and gain. The proposed antenna design has tackled the issues faced by the conventional antenna and holds promise for real-time applications in healthcare, military, sports, and identification systems. It addresses critical challenges such as Specific Absorption Rate (SAR) and efficiency, ensuring optimal performance when interacting with human body tissues. Moreover, the experimental results demonstrate that the simulated and fabricated results exhibit similar deviations, confirming that the antenna is suitable for real-world applications.
The preparation of nanoparticles through sustainable chemical practices offers an eco-friendly and feasible route to advanced nanomaterials. In this study, gold nanoparticles (Au-NPs) were synthesized using Garcinia mangostana (GM) peel extract, which is rich in antioxidants and provides biomolecules acting as bio-reductants, capping, and stabilizing agents. The synthesized Au-NPs were characterized by X-Ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV–Vis spectroscopy, scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. XRD confirmed the face-centered cubic structure of Au-NPs, while UV–Vis spectra exhibited a characteristic absorption peak around 540 nm. TEM images revealed polydispersed nanoparticles with diverse shapes and sizes. FTIR analysis indicated the presence of phenols, flavonoids, benzophenones, and anthocyanins, suggesting their role in the reduction and stabilization of Au-NPs. The catalytic activity of Au-NPs was demonstrated by the effective degradation of Fuchsin Basic dye, achieving up to 89
Accurate air pollution prediction needs systems that monitor diverse environmental data, adapt to changing pollution patterns, and ensure secure real-time communication. Existing systems struggle to capture distributed sensor interactions and dynamic environmental variations effectively. To address this problem, the study proposes an Adaptive and Efficient Air Pollution Monitoring Network (AEPM-Net) incorporating Metal Oxide Semiconductor (MOS) nanosensors with an average particle size ranging from 20 to 40 nm for sensitive air-quality monitoring. The system monitors levels of Carbon Monoxide CO , Nitrogen Dioxide NO_2 , Sulfur Dioxide SO_2 , Nitric Oxide NO , Volatile Organic Compound VOC , and Particulate Matter PM , which includes fine particulate matter PM2.5 and coarse particulate matter PM10 , in different locations. The system uses SPECK encryption and additive Lightweight Homomorphic Encryption (LHE) to protect collected pollutant data, while noise filtering and modified Z-score normalization methods help to stabilize the data. The Spatio-Temporal Data Processing Module (ST-DPM) analyzes nanosensor data across time and space by capturing correlations among distributed sensors and environmental variations. The Hierarchical Attention Mechanism (HAM) prioritizes important spatio-temporal features to improve prediction accuracy. The Dynamic Adaptation Module (DAM) adjusts parameters in real time, while the Pollution Level Prediction Module (PLPM) forecasts pollutant levels with uncertainty handling. Over 90 days, the model achieved R² values of 0.973 CO , 0.974 NO , 0.975 NO_2 , 0.978 SO_2 , 0.971 VOC , 0.969 PM2.5 , and 0.967 PM10 . The proposed framework supports environmental sustainability by enabling early detection of hazardous pollutants, improving urban air quality monitoring, and assisting effective pollution control to protect public health.
Fog computing gives various kinds of properties over the internet which allows utilizing different types of services from industries. In these cloud architectures, the key bottleneck is their restricted scalability and therefore incapacity to meet the requirements of centralized computing environments focused on the Internet of Things (IoT). The vital clarification for this is that inertness touchy applications, for example, wellbeing observing and observation frameworks currently need processing over a lot of information (Big Data) moved to concentrated data set and from data set to cloud server farms which prompts drop in execution of such frame works. Fog and edge computing latest paradigms offer revolutionary technologies by taking user services closer and delivering low latency and energy-productive information preparing arrangements contrasted with cloud areas. However the latest fog models have several drawbacks and concentrate on either outcome precision or it may down the time of response but not under narrow perspective. The proposed novel system called ABFog which integrating with edge computing devices in deep learning and it is useful to analyze Heart disease automatically in real-time application. Fog computing is enabled in cloud framework to utilize the Fog Bus which is used to convey the accuracy to present the proposed model. ABFog is useful to provide best quality of service in various configuration modes or to predict accuracy as needed to assort in different situations and for various users prerequisites.
