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The present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles to optimize thermal performance and ensure long-term stability. CuO (1 wt.%) and TiO₂ (0.1, 0.3, 0.5,0.7 wt%) were introduced into the matrix, and the thermal properties were systematically evaluated using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) before and after 500 thermal cycles. The optimal composition, consisting of 1 wt% CuO and 0.3 wt% TiO₂, demonstrated an initial latent heat capacity of 117 J/g, which increased to 123 J/g post-cycling, indicating exceptional thermal stability and phase retention. To further enhance predictive capabilities and reduce experimental costs, an artificial neural network (ANN) model was developed using the Keras API in Python to estimate thermal behaviour. The model achieved a high coefficient of determination (R2 = 0.9479) and a low root-mean-square error (RMSE = 2.0307), underscoring its accuracy and reliability. These findings establish the efficacy of hybrid nanoparticle incorporation in improving SS-PCMs’ thermal properties and emphasise the viability of machine learning as a robust predictive tool, mitigating the time and economic constraints associated with extensive experimental investigations.

Increasing evidence demonstrates a robust link between environmental pollutants and allergic reactions, with air and indoor pollution exacerbating respiratory allergies and climate change intensifying seasonal allergies. Comprehensive action, including government regulations, public awareness, and individual efforts, is essential to mitigate pollution's impact on allergies and safeguard public health and ecological balance. Recent findings indicate a strong correlation between environmental pollutants and allergic reactions, with air pollution from vehicular emissions and industrial activities exacerbating respiratory allergies like asthma and allergic rhinitis. Additionally, indoor pollutants such as mold and volatile organic compounds are significant triggers of allergic responses, especially among vulnerable populations. Furthermore, climate change, driven by pollution, is intensifying seasonal allergies due to altered weather patterns and increased pollen production. This review emphasizes the critical importance of addressing pollution and allergies, highlighting the growing concerns in contemporary society. This review highlights the urgent need to address pollution and allergies, emphasizing their increasing significance in modern society and outlining effective allergy management strategies.

Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate the sporadic nature of renewable energy sources into the existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods to bolster the grid's stability, flexibility, and dispatchability. This paper presents a dual-pronged, innovative approach to maximize income in the day-ahead power market through VPP. On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue's superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. The proposed method effectively synergizes the concepts of VPP, energy storage, and AOLSTM to yield more substantial income in the day-ahead electricity market. Notably, the introduced AOLSTM approach demonstrates minimal error metrics compared to conventional methods such as persistence, Gradient Boost, and Random Forest.

Runoff efficiency (RE) represents the potential of a basin to generate runoff in response to precipitation and it varies based on climatology and physiography. It is a key metric that enables hydrologists to compare the hydrologic responses of basins across diverse climates and landscapes. In the large river basins of South Asia, RE plays a key role in floods and drought dynamics but has not been studied systematically. This study employs an integrated hydrologic-hydrodynamic modeling system, the Indian Land Data Assimilation System (ILDAS), to investigate the spatiotemporal variability of RE and their climatological and physiographic controls. The spatial variability of RE is mainly driven by mean precipitation and soil type. High RE is observed in the Central Indian basins, including Narmada, Brahmani, Godavari, and Mahanadi, due to high monsoon rainfall distribution and low permeability soils. However, large basins such as Brahmaputra, Ganga, and Subarnarekha exhibit low RE despite high monsoon rainfall, largely due to moderately permeable soil and significant baseflow contributions. Temporal variability in monthly RE influenced by event precipitation magnitude, antecedent soil moisture, and seasonal cycles, with the El Niño-Southern Oscillation (ENSO) exerting a notable influence at the interannual scale. La Niña events, particularly when coinciding with the monsoon season, enhance RE over the central eastern, central, and peninsular India, driven by ENSO’s amplified impact on runoff relative to precipitation. Conversely, during the winter season in northern India, ENSO effects on precipitation don’t translate into RE. Long-term trend analysis shows a rising trend in RE across several basins, during monsoon and pre-monsoon seasons, indicating an accelerated response of water cycle response linked to climate change. Notably, a positive trend in RE is observed in West Flowing Kachh-Sabarmati basins in the monsoon, pre-monsoon, and winter. Our findings enhance our understanding of the trends and drivers of RE and underscore the need for adaptive water management strategies in response to evolving patterns.

Textile dyes pose a major environmental threat due to their toxicity, persistence in water bodies, and resistance to conventional wastewater treatment. To address this, researchers have explored biological and physicochemical degradation methods, focusing on microbial, photolytic, and nanoparticle-mediated approaches, among others. Microbial degradation depends on fungi, bacteria, yeasts, and algae, utilizing enzymatic pathways involving oxidoreductases like laccases, peroxidases, and azoreductases to breakdown or modify complex dye molecules. Photolytic degradation employs hydroxyl radical generation and electron-hole pair formation, while nanoparticle-mediated degradation utilizes titanium dioxide (TiO2), zinc oxide (ZnO), and silver (Ag) nanoparticles to enhance dye removal. To improve efficiency, microbial consortia have been developed to enhance decolorization and mineralization, offering a cost-effective and eco-friendly alternative to physicochemical methods. Photocatalytic degradation, particularly using TiO2, harnesses light energy for dye breakdown. Research advancements focus on shifting TiO2 activation from UV to visible light through doping and composite materials, while optimizing surface area and mesoporosity for better adsorption. Nanoparticle-mediated approaches benefit from a high surface area and rapid adsorption, with ongoing improvements in synthesis, functionalization, and reusability, particularly through magnetic nanoparticle integration. These emerging technologies provide sustainable solutions for dye degradation. The primary aim of this review is to comprehensively evaluate and synthesize current research and advancements in the degradation of azo dyes through microbial methods, photolytic processes, and nanotechnology-based approaches. The review also provides detailed information on salient mechanistic aspects of these methods, efficiencies, advantages, challenges, and potential applications in industrial and environmental contexts.

IntroductionPhotovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption.MethodThis paper proposes a hybrid MPPT technique that integrates the Pelican Optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT.Results and DiscussionTo validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards.

The NTA–Ni@Fe3O4 NPs can capture enzymes from cell lysates and microbial culture media. We developed a model system to show the efficacy of immobilizing and recycling biomass-degrading enzymes known as cellulases.

Off-grid water pumping systems (OGWPS) have become an increasingly popular area of research in the search for sustainable energy solutions. This paper presents a finite element method (FEM)-based design and analysis of Brushless-DC (BLDC) and Switched Reluctance Motors (SRM) designed for low-power water pumping applications. Utilizing adaptive finite element analysis (FEA), both motors were designed with identical ratings and design parameters to ensure a fair comparison. The design geometries adhere to the NEMA 42 standard. An (n + 1) switch converter topology was implemented to energize SRM phases, while a Cuk converter coupled with a three-phase voltage source inverter (VSI) was used to power the BLDC motor. The study provides a comprehensive comparative analysis of the torque profiles of both motor types under identical operating conditions. The BLDC motor achieved a maximum torque of 11.5 Nm and an efficiency of 91.9%, while the SRM demonstrated a maximum torque of 3.8 Nm and an efficiency of 94.6%. The torque ripple of the BLDC motor was significantly lower (0.73 pu) compared to the SRM (1.19 pu), indicating smoother operation. Simulation results obtained using advanced computational electromagnetic tools highlight the performance efficiencies and potential advantages of each motor type for off-grid water pumping systems. This research investigates the viability of both BLDC and SRM technologies in enhancing the efficiency and reliability of OGWPS, with the BLDC motor showing superior performance in terms of torque and operational smoothness. The simulation results of converter topologies used for respective motors have been further validated using experimentation on respective prototype motors.