Velammal College of Engineering and Technology

Background Catechol (CC) is an important phenolic molecule that plays an important role in various industrial applications. At the same time, it is a significant environmental pollutant. The rapid, sensitive, and selective detection of CC remains challenging because of the coexistence of structurally similar phenolic impurities. This study proposes an electrochemical method for CC detection using LaBi2O4 and LaBi2O4/reduced graphene oxide (LaBi2O4@rGO) nanocomposites. Methods The nanocomposites were synthesized using a simple hydrothermal technique, which led to improved electrochemical properties owing to the synergistic effect between LaBi2O4 and rGO. The structural and morphological properties of the LaBi2O4@rGO nanocomposite were investigated using various techniques. Electrochemical analyses were performed using cyclic voltammetry and differential pulse voltammetry, which revealed considerable sensitivity and selectivity for CC detection. Significant findings The LaBi2O4@rGO incorporated GC electrode possessed a wide linear detection range (1–700 µM), with a low detection limit (0.15 µM). Moreover, the newly fabricated electrode exhibited high stability, repeatability, and reproducibility, making it suitable for environmental applications. Its effectiveness was further validated by detecting CC in real water samples, such as tap and river water, with a 99% recovery rate, thereby demonstrating its immense potential for practical environmental monitoring.

The rapid digitalization of energy systems through smart meters provides vast data streams that, when contextualized with socioeconomic information, can reveal the underlying behavioral mechanisms of electricity use. However, standard machine learning pipelines often overlook the non-Euclidean relationships embedded within social structures and spatial proximity, leading to limited predictive reliability. This paper presents a Hypergraph-Driven Hybrid Intelligence Framework (HHIF) that synthesizes hypergraph attention, graph convolution, and temporal learning to create an adaptive model for residential load forecasting. The proposed system encodes complex socio-energy interactions using Hypergraph Attention Networks (HANs), integrates K-means clustering for structure-aware feature extraction, and fuses GCN and LSTM modules for joint spatial–temporal optimization. Experiments on multi-city smart meter datasets highlight substantial gains in accuracy, stability under noise, and interpretability, underscoring the framework’s applicability for grid management, peak load mitigation, and energy equity research.

Developing efficient and cost-effective catalysts for industrial-scale hydrogen (H2) and oxygen (O2) production is crucial for the clean energy transition. Atomically dispersed single-atom catalysts (AD SACs) (e.g., iron (Fe), cobalt (Co), and nickel (Ni)) without precious metals are particularly promising, offering 100% atomic utilization and optimum catalytic efficiency. Nonetheless, key challenges remain, including the suppression of atom aggregation, the optimization of support structures, and the maintenance of stability. Moreover, industrial catalysis demonstrates that active components with suitable supports are critical, and catalytic efficacy often increases as the size of the active components decreases, opening new frontiers in catalyst design. Thus, metal-organic frameworks (MOFs) and MXenes have emerged as effective anchoring matrices, enabling modulation of electronic structures, rapid charge transfer, and enhanced stabilization of single metal atoms. This review highlights recent progress in the development of AD SACs anchored on MOFs and MXenes, emphasizing the fundamental parameters governing hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performance. Furthermore, we integrate theoretical insights from Density Functional Theory (DFT) with advanced operando and in-situ characterization techniques to provide a comprehensive understanding of the mechanistic origins governing HER/OER activity. We anticipate that these insights will contribute to improving water-splitting efficiency and sustainability, which are vital for advancing the clean energy revolution. Finally, this review outlines future directions for the development of high-efficiency, cost-effective MXene- and MOF-based SAC catalysts, underscoring their potential in clean energy production.

Microgrids based on renewable energy have gained prominence as environmental sustainability has received more attention globally. However, the fluctuating nature of renewable energy sources (RES), erratic energy consumption, and the dynamic influence of EVs on microgrid security pose a threat to their stability. This manuscript proposes a hybrid energy management strategy (EMS) for a multi-source microgrid that incorporates electric vehicles (EVs), RES, and an adaptive fractional order proportional-integral-derivative (AFOPID) controller to address these issues. The proposed approach combines the Portia Spider Algorithm (PSA) and Temporal Dynamic Graph Neural Network (TDGNN), which is termed the PSA-TDGNN method. The primary goal of the proposed method is to develop system efficiency, minimize grid dependency, minimize estimation error, and maintain a stable and reliable power supply for connected loads. The TDGNN is used to predict performance under high EV penetration levels, while the PSA is employed to optimize the AFOPID controller's gain factors. The proposed technique is assessed and compared utilizing the MATLAB platform to other existing approaches. The proposed approach yields better outcomes compared to existing methods like the Advanced-Sine Cosine Algorithm (a-SCA), Adaptive Salp Swarm Algorithm (ASSA), and Bald Eagle Search (BES). The proposed approach achieves an error of 0.59%, efficiency of 97%, and computational complexity of 18.23 s. These results demonstrate that the proposed technique achieves better performance and enhances energy management in multi-source microgrids based on EVs compared to existing methods.

Worldwide, breast cancer is the primary cause of deaths in women. Because of the restrictions of current clinical imaging techniques, such as mammography, ultrasound scanning, and magnetic resonance scanning, as well as harmful radiation, which is expensive and a hindrance to patients, researchers have been motivated to investigate alternate methods, including the use of microwave components, to detect breast tumors in the early stages. This study is focused on the implementation of an antenna for the detection of small tumors using a novel material as a substrate at an operating frequency of 2.42 GHz. The material utilized is a textile fabric woven using a plain pattern of jute thread with cotton threads. The proposed antenna has overall dimensions of 50 mm × 62 mm × 0.71 mm 3 and demonstrates a peak gain of 3.56 dBi, with a radiation efficiency exceeding 70% throughout the operating band. Breast models with and without tumor cells with tumor radii of 2, 3, and 5 mm were designed using Computer Simulation Technology software. The designed antenna was placed on breast models with and without tumors and simulated. The return loss, gain, current density, E -field, and H -field distributions exhibited deviations when the antenna was placed on the breast model with a tumor. However, the specific absorption rate (<1 W/kg) did not exceed the standard limit. The antenna was fabricated and measured using a vector network analyzer. The measured results show that the return loss value (−22.3 dB) was close to the simulated value (−26.3 dB).