Automated malaria diagnosis using microscopic blood smear images has emerged as an effective alternative to manual examination; however, existing deep learning approaches predominantly rely on spatial-domain representations and conventional optimization objectives, limiting their ability to exploit complementary spectral information and discriminate challenging samples. This paper proposes WaveFusionNet, a novel dual-branch deep learning framework that jointly learns spatial and frequency-domain representations for robust malaria cell classification. The proposed architecture introduces two original contributions. First, a Frequency-Gated Spatial Modulation (FGSM) module computes wavelet detail-energy maps to generate adaptive spatial gates, enabling frequency information to selectively enhance informative spatial features while suppressing irrelevant background responses. Second, a Wavelet Energy-Guided Contrastive Loss (WEGCL) exploits eight-dimensional wavelet energy signatures to identify spectrally hard positive and negative pairs, thereby strengthening feature discrimination through adaptive contrastive learning. Furthermore, a Cross-Domain Attention Fusion module effectively integrates complementary spatial and frequency representations using multi-head cross-attention, while Adaptive Channel-Spatial Attention dynamically balances channel-wise and spatial feature refinement throughout the network. Extensive experiments on the benchmark NIH Malaria Cell Images dataset (27,558 images) demonstrate that WaveFusionNet achieves 98.47% accuracy, 98.60% recall, and an AUC of 0.9978, consistently outperforming eight state-of-the-art deep learning models. Five-fold cross-validation confirms excellent statistical robustness, and Grad-CAM visualizations verify that the proposed framework accurately localizes clinically relevant parasitic regions. Despite its superior predictive capability, the model remains computationally efficient, with only 15.1 million parameters and an inference latency of 7.2 ms, making it well-suited for real-time deployment in resource-constrained point-of-care environments. The proposed WaveFusionNet provides an effective and interpretable spatial-frequency learning paradigm that advances automated malaria diagnosis through synergistic feature fusion and wavelet-guided representation learning.
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.
Developing multifunctional catalysts that integrate solar hydrogen generation with CO2 utilization is essential for advancing carbon neutral energy technologies. In this study, a multifunctional V2O5/polydopamine nanocomposite (V2O5/PDA NC) was developed by first synthesizing V2O5 nanoparticles (NPs) via a controlled sol gel process, followed by surface functionalization through dopamine self polymerization. Structural and morphological analyses (XRD, FTIR, UV-Vis, SEM, TEM, and zeta potential) confirmed the formation of crystalline alpha-V2O5 with PDA interfacial functionalization. The pristine V2O5 NPs exhibited a mean particle size of 11.10 +/- 4.27 nm (median: 10.27 nm), which decreased to 7.46 +/- 2.57 nm (median: 7.10 nm) after PDA modification, indicating improved dispersion and inhibited agglomeration. The PDA coating narrowed the optical band gap from 2.06 to 1.71 eV and improved dispersion stability as evidenced by the measured surface charge (zeta potential: -52 mV for V2O5). The pi-conjugated PDA interface promoted efficient charge transfer, suppressed electron hole recombination, and extended visible light absorption. After 8 h of simulated solar irradiation, the cumulative H-2 yield increased by 35% from 8.46 to 11.44 mmol H-2 g(-)& sup1;. In CO2 methanation, the V2O5/PDA NC achieved 95.76% CO2 conversion with high CH4 selectivity at 330 degrees C, surpassing pristine V2O5 (74.73%). These results demonstrate that PDA-mediated interfacial regulation provides a scalable route to enhance charge utilization and surface reactivity of sol-gel-derived V2O5 for high-efficiency solar hydrogen production and CO2 methanation.
Loofah sponges, traditionally grown in Asia for centuries, could be a sustainable and abundant source of carbon material for energy storage applications owing to their lignocellulosic fibrous structure. Herein, waste loofah sponges were transformed into activated carbon (LAC) for use as high-performance supercapacitor electrodes. The capacitive performance of the LAC was further enhanced by hybridizing it with nanostructured copper oxide (Cu2O). A two-step process, involving the pyrolysis of copper-salt-soaked loofah sponge biomass and CO2-activation process, was carried out to prepare a Cu2O nanoparticle-anchored activated-LAC composite (Cu2O@LAC). The as-prepared activated Cu2O@LAC composite possesses a high specific surface area of 1757.63 m2 g−1 with a large fraction of mesopores, which allows large molecules or ions to easily diffuse through the material to reach active Cu2O sites, enhancing its capacitance with improved rate performance. The Cu2O@LAC electrode achieved a high gravimetric specific capacitance of up to 520 F g− 1 at 0.2 A g−1, along with excellent capacitance retention of 91.7
Schiff bases are privileged azomethine pharmacophores with diverse bioactivity. In this report, two new hydrazine-derived Schiff bases, L1 and L2, were synthesized. The structures were confirmed by experimental approaches involving IR, UV-vis, 1H, and 1 3C NMR spectroscopy and showed strong correlation with theoretical computational predictions. Molecular reactivity was explored using DFT (B3LYP/6-311G(d,p), SMD/water) through frontier orbitals, electrostatic potential, and global reactivity descriptors. Antibacterial evaluation against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa showed strain-selective potency: L1 was most active against S. aureus and E. coli (MIC 4.9 and 6.5 & micro;g/mL), while L2 displayed moderate activity against P. aeruginosa (MIC 8.4 & micro;g/mL). Ciprofloxacin remained the strongest reference (MIC 0.049-0.521 & micro;g/mL). Docking with DNA gyrase (PDB ID 6F86) indicated favorable binding for L1 (-6.4 kcal/mol) and L2 (-6.0 kcal/mol) vs. ciprofloxacin (-7.0 kcal/mol). ADMET predictions supported acceptable drug-likeness and low toxicity. These results highlight hydrazine-based Schiff bases as promising leads for antibacterial design against multidrug resistance.
