The demand for clean water is rapidly growing, which has prompted further development of nanomaterials for environmental purification and biomedical research. Photocatalysis is a highly promising oxidation method for the removal of organic and biological pollutants. It is quite evident that there is a serious and pressing demand to design economic treatment techniques, which are cheap and energy independent. In this study, we described the fabrication of Z-scheme heterostructure Er doped ZrO2/Bi2WO6 that effectively enhances visible-light-driven charge separation and redox ability, leading to superior photocatalytic degradation and antibacterial performance. The dual-functionality and high mineralization efficiency demonstrated in this study highlight the potential of the developed photocatalyst for sustainable wastewater treatment and biomedical-related environmental applications. The composite and its constituents were synthesized by hydrothermal approach and characterized in terms of their structural, optical, functional, morphological, photocatalytic and antibacterial properties. The optimized Er-doped ZrO2/Bi2WO6 nanocomposite exhibited excellent photocatalytic performance, achieving 97.02% degradation of the moxifloxacin (MOX) drug under visible light and showed good antibacterial capability against four bacterial strains the order of Escherichia coli > Bacillus fortis > Staphylococcus aureus > Streptococcus canis. Reactive-species scavenging analysis proved that the role of superoxide radicals (•O2−) and hydroxyl (•OH) plays a significant role in the photocatalytic degradation of MOX. The current study highlights the enhanced photocatalytic and antibacterial activity of the Z-scheme Er doped ZrO2/Bi2WO6 heterostructure due to their design and engineering, with applications in multifunctional industries, particularly environmental cleaning and biomedical research.
Accurate energy production prediction is a critical challenge in modern renewable energy systems due to the inherent variability and complexity of influencing factors. This study proposes an optimized hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) enhanced by Genetic Algorithm (GA)-based hyperparameter optimization to improve predictive performance. A publicly available renewable energy dataset comprising 15,000 records and 13 features was utilized, where Z-score normalization was applied to standardize input variables and enhance model convergence. To reduce dimensionality and retain the most informative attributes, Binary Particle Swarm Optimization (BPSO) was employed for feature selection and benchmarked against Binary Grey Wolf Optimization (BGWO), Binary Whale Optimization Algorithm (BWOA), and Binary Butterfly Optimization Algorithm (BBOA), demonstrating superior performance in terms of fitness and error metrics. The dataset was partitioned into training (70%), validation (20%), and testing (10%) subsets to ensure robust generalization. The proposed CNN-RNN model was further optimized using GA and compared with several baseline regression models, including Gradient Boosting Regressor (GBR), Extra Trees Regressor (ETR), Decision Tree Regressor (DTR), K-Nearest Neighbors Regressor (KNNR), and Dummy Regressor (DR). Experimental results indicate that the optimized GA-CNN-RNN model significantly outperformed all competing models, achieving the lowest Mean Squared Error (MSE = 0.0098), Mean Absolute Error (MAE = 0.0791), Median Absolute Error (MedAE = 0.0683), Mean Absolute Percentage Error (MAPE = 0.0090), and the highest coefficient of determination (R2 = 99.85%). Comparative analysis with alternative metaheuristic optimizers, including Artificial Bee Colony (ABC), Firefly Algorithm (FA), and Dragonfly Algorithm (DA), further confirmed the superiority of GA in enhancing model performance.
This comprehensive review presents a thorough examination of recent advances in nanoemulsion (NE) green technology, focusing on biomass-assisted synthesis, characterization, and the diverse biomedical implications of these nanoscale emulsions. NEs, characterized by their minute droplet sizes and kinetic stability, have garnered considerable attention due to their potential applications across various biomedical fields. This review presents a comprehensive analysis of state-of-the-art synthesis methods, including mini-emulsion polymerization, NE–solvent evaporation, spontaneous emulsification, sol–gel techniques, and innovative strategies for producing complex multicomponent materials. Emphasis is placed on the evolution of synthetic approaches, offering insights into the current landscape of NE production. In exploring the biomedical applications, the study categorizes nanocarriers formed within NEs, distinguishing between polymeric, inorganic, and hybrid nanocarriers based on their chemical composition. Noteworthy advancements in synthetic strategies are outlined for each category, showcasing the dynamic nature of NEs technology. A key highlight is the discussion of emerging trends in biomedical applications, spanning medicine, food, agriculture, cosmetics, and environmental science. Specific attention is given to the role of NEs in nanofiltration, elucidating their effectiveness in removing diverse pharmaceuticals through polyamide nano-filters. Moreover, the manuscript delves into the pivotal role of NEs in bioremediation, addressing hazardous substances such as PFASs through adsorption, photo-degradation/defluorination, and other innovative mechanisms. This review aims to provide a contemporary overview of green NE technologies, offering valuable insights for researchers, scientists, and practitioners in nanotechnology, pharmaceuticals, and biomedical sciences.
This study evaluated dietary quinoa seed extract (QSE) in Nile tilapia fingerlings (initial weight 6.32 ± 0.09 g). Fish were fed a basal diet (CTR) or a diet supplemented with QSE at 0.25, 0.5, or 1.0 g kg⁻¹ for 56 days. Results showed that 1.0 g kg⁻¹ QSE significantly improved final body weight (33.46 vs. 27.76 g in CTR), weight gain, feed conversion ratio, and protein efficiency ratio (P < 0.05). Furthermore, serum total protein and globulin increased, while liver enzymes (ALT, AST) decreased in QSE-fed fish. Also, digestive enzyme activities (amylase, lipase) and antioxidant capacity (catalase, GSH) were enhanced. Notably, all QSE-supplemented groups showed 100
Ruthenium (Ru) incorporation represents an effective strategy for tuning the optoelectronic properties of titanium dioxide (TiO2) for solar energy conversion applications. In this study, Ru-doped TiO2 samples with varying dopant concentrations (0–5
