Search Results (2,701)

The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RH_w) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH₄NO₃). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

Green synthesis of copper oxide (CuO) nanoparticles offers a sustainable alternative to conventional chemical methods that often involve toxic reagents and harsh conditions. This study investigates the use of Tithonia diversifolia, an invasive species in Sri Lanka, as a bioreductant for the eco-friendly fabrication of CuO nanoparticles. Using copper sulfate (CuSO₄·5H₂O) as a precursor, eight treatments were conducted by varying precursor concentration, temperature, and reaction time to determine optimal conditions. A visible color change in the reaction mixture initially indicated nanoparticle formation. Among all the conditions, treatment T4 (5 mM CuSO₄, 80 °C, 2 h) yielded the most favorable results in terms of stability, morphology, and crystallinity. UV-Vis spectroscopic analysis confirmed the synthesis, with absorbance peaks between 265 and 285 nm. FTIR analysis revealed organic functional groups and characteristic metal–oxygen vibrations in the fingerprint region (500–650 cm⁻¹), confirming formation. SEM imaging showed that particles were mainly spherical to polygonal, averaging 125–150 nm. However, dynamic light scattering showed larger diameters (~240 nm) due to surface capping agents. Zeta potential values ranged from −16.0 to −28.0 mV, indicating stability. XRD data revealed partial crystallinity with CuO-specific peaks. These findings support the potential of T. diversifolia in green nanoparticle synthesis, suggesting a low-cost, eco-conscious strategy for future applications. Full article

Persistent reactive azo dyes released from textile finishing are a serious threat to water systems, but effective methods using sunlight to break them down are still limited. Everzol Yellow 3RS (EY-3RS) is particularly recalcitrant: past studies have relied almost exclusively on physical adsorption onto natural or modified clays and zeolites, and no photocatalytic pathway employing engineered nanomaterials has been documented to date. This study reports the synthesis, characterization, and performance of a visible-active ternary nanocomposite, Cu₄O₃/ZrO₂/TiO₂, prepared hydrothermally alongside its binary (Cu₄O₃/ZrO₂) and rutile TiO₂ counterparts. XRD, FT-IR, SEM-EDX, UV-Vis, and PL analyses confirm a heterostructured architecture with a narrowed optical bandgap of 2.91 eV, efficient charge separation, and a mesoporous nanosphere-in-matrix morphology. Photocatalytic tests conducted under midsummer sunlight reveal that the ternary catalyst removes 91.41% of 40 ppm EY-3RS within 100 min, markedly surpassing the binary catalyst (86.65%) and TiO₂ (81.48%). Activity trends persist across a wide range of operational variables, including dye concentrations (20–100 ppm), catalyst dosages (10–40 mg), pH levels (3–11), and irradiation times (up to 100 min). The material retains ≈ 93% of its initial efficiency after four consecutive cycles, evidencing good reusability. This work introduces the first nanophotocatalytic strategy for EY-3RS degradation and underscores the promise of multi-oxide heterojunctions for solar-driven remediation of colored effluents. Full article

Background/Objectives: The rapid emergence of multidrug-resistant bacterial infections demands innovative non-antibiotic therapeutic strategies. Dual-modal photoresponse therapy integrating photodynamic (PDT) and photothermal (PTT) effects offers a promising rapid antibacterial approach, yet designing single-material systems with synergistic enhancement remains challenging. This study aims to develop uniform Cu-based metal–organic framework micrometer cubes (Cu-BN) for efficient PDT/PTT synergy. Methods: Cu-BN cubes were synthesized via a one-step hydrothermal method using Cu(NO₃)₂ and 2-amino-p-benzoic acid. The material’s dual-mode responsiveness to visible light (420 nm) and near-infrared light (808 nm) was characterized through UV–Vis spectroscopy, photothermal profiling, and reactive oxygen species (ROS) generation assays. Antibacterial efficacy against multidrug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was quantified via colony counting under dual-light irradiation. Results: Under synergistic 420 + 808 nm irradiation for 15 min, Cu-BN (200 μg/mL) achieved rapid eradication of multidrug-resistant E. coli (99.94%) and S. aureus (99.83%). The material reached 58.6 °C under dual-light exposure, significantly exceeding single-light performance. Photodynamic analysis confirmed a 78.7% singlet oxygen (¹O₂) conversion rate. This enhancement stems from PTT-induced membrane permeabilization accelerating ROS diffusion, while PDT-generated ROS sensitized bacteria to thermal damage. Conclusions: This integrated design enables spatiotemporal PDT/PTT synergy within a single Cu-BN system, establishing a new paradigm for rapid-acting, broad-spectrum non-antibiotic antimicrobials. The work provides critical insights for developing light-responsive biomaterials against drug-resistant infections. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

The hybrid inorganic–organic material concept plays a bold role in multifunctional materials, combining different features on one platform. Once varying properties coexist without cancelling each other on one matrix, a new type of supermaterial can be formed. This concept showed that silver nanoparticles can be embedded together with inorganic and organic surface coatings and silicon quantum dots for symbiotic antibacterial character and UV-excited visible light fluorescent features. Additionally, fluorosilane material can be coupled with this prepolymeric structure to add the hydrophobic feature, showing water contact angles around 120°, providing self-cleaning features. Optical properties of the components and the final material were investigated by UV-Vis spectroscopy and PL analysis. Atomic investigations and structural variations were detected by XPS, SEM, and EDX atomic mapping methods, correcting the atomic entities inside the coating. FT-IR tracked surface features, and statistical analysis of the quantum dots and nanoparticles was conducted. Multifunctional final materials showed antibacterial properties against E. coli and S. aureus, exhibiting self-cleaning features with high surface contact angles and visible light fluorescence due to the silicon quantum dot incorporation into the sol-gel-produced nanocomposite hybrid structure. Full article

Commercially available bismuth subcarbonate (Bi₂O₂CO₃) was treated with nitric acid and the surfactant cetyltrimethylammonium bromide. The treated catalysts exhibited enhanced photocatalytic activity compared to pure Bi₂O₂CO₃ in the decolorization of rhodamine B (RhB) under visible light irradiation. The absorbance at 554 nm gradually decreased over time and disappeared completely within 80 min. The crystal structure, morphology, and optical properties of the samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-Vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) spectroscopy. The improved photocatalytic activity of the treated catalysts was attributed to partial carbonate removal and the formation of Bi⁵⁺ species. Scavenger experiments indicated that superoxide radicals (·O₂⁻) and photogenerated holes (h⁺) played significant roles in the photocatalytic decolorization of RhB. Full article

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article

The transition to sustainable energy systems demands efficient recycling methods for critical raw materials like lithium. In this study, we present a new class of pH- and light-switchable flotation collectors based on isomeric derivatives of the natural product Punicine, termed inverse Punicines. These amphoteric molecules were synthesized via a straightforward four-step route and structurally tuned for hydrophobization by alkylation. Their performance as collectors was evaluated in microflotation experiments of lithium aluminate (LiAlO₂) and silicate matrix minerals such as melilite and calcium silicate. Characterization techniques including ultraviolet-visible (UV-Vis), nuclear magnetic resonance (NMR) and electron spin resonance (ESR) spectroscopy as well as contact angle, zeta potential (ζ potential) and microflotation experiments revealed strong pH- and structure-dependent interactions with mineral surfaces. Notably, N-alkylated inverse Punicine derivatives showed high flotation yields for LiAlO₂ at pH of 11, with a derivative possessing a dodecyl group attached to the nitrogen as collector achieving up to 86% recovery (collector conc. 0.06 mmol/L). Preliminary separation tests showed Li upgrading from 5.27% to 6.95%. Radical formation and light-response behavior were confirmed by ESR and flotation tests under different illumination conditions. These results demonstrate the potential of inverse Punicines as tunable, sustainable flotation reagents for advanced lithium recycling from complex slag systems. Full article

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

Although Zn (II)-(−)-Epigallocatechin gallate (EGCg) complex (Zn-EGCg) is known for its promising bioactivities, little attention has been paid to its incorporation into daily green tea consumption. In this study, we aimed to incorporate Zn (II) into green tea extract to promote the formation of Zn-EGCg complex within the tea matrix. We then investigated how the formation of Zn-complexed green tea extract (Zn-GTE) influences the gut microbiota in a Western diet (WD)-fed mouse model. Structural analyses using ultraviolet–visible spectroscopy (UV–Vis), Fourier-transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (¹H NMR), and powder X-ray diffraction (PXRD) suggested that Zn (II) interacted with hydroxyl groups of polyphenols within the extract, consistent with Zn-EGCg formation, although the complex could not be unequivocally identified. Under intake levels equivalent to daily consumption, Zn-GTE administration restored WD-induced reductions in alpha-diversity and resulted in a distinct microbial composition compared to treatment with green tea extract (GTE) or Zn alone, as shown by beta-diversity analysis. Linear discriminant analysis Effect Size (LEfSe) analysis revealed increased abundances of bacterial taxa belonging to o_Clostridiales, o_Bacteroidales, and f_Rikenellaceae, and decreased abundances of g_Akkermansia in the Zn-GTE group compared to the GTE group. These findings highlight that Zn-GTE, prepared via Zn (II) supplementation to green tea, may exert distinct microbiota-modulating effects compared to its individual components. This study provides new insights into the role of dietary metal–polyphenol complexes, offering a food-based platform for studying metal–polyphenol interactions under physiologically relevant conditions. Full article

Water pollution caused by increasing industrial and human activity remains a serious environmental challenge, especially due to the persistence of organic contaminants in aquatic systems. Photocatalysis offers a promising and eco-friendly solution, but in the case of bismuth oxide (Bi₂O₃) there is still a limited understanding of how structural and morphological features influence photocatalytic performance. In this work, a straightforward hydrothermal synthesis method followed by controlled calcination was developed to produce phase-pure α- and β-Bi₂O₃ with tunable morphologies. By varying the hydrothermal temperature and reaction time, distinct structures were successfully obtained, including flower-like, broccoli-like, and fused morphologies. XRD analyses showed that the final crystal phase depends solely on the calcination temperature, with β-Bi₂O₃ forming at 350 °C and α-Bi₂O₃ at 500 °C. SEM and BET analyses confirmed that morphology and surface area are strongly influenced by the hydrothermal conditions, with the flower-like β-Bi₂O₃ exhibiting the highest surface area. UV–Vis spectroscopy revealed that β-Bi₂O₃ also has a lower bandgap than its α counterpart, making it more responsive to visible light. Photocatalytic tests using Rhodamine B showed that the flower-like β-Bi₂O₃ achieved the highest degradation efficiency (81% in 4 h). Kinetic analysis followed pseudo-first-order behavior, and radical scavenging experiments identified hydroxyl radicals, superoxide radicals, and holes as key active species. The catalyst also demonstrated excellent stability and reusability. Additionally, Methyl Orange (MO), a more stable and persistent azo dye, was selected as a second model pollutant. The flower-like β-Bi₂O₃ catalyst achieved 73% degradation of MO at pH = 7 and complete removal under acidic conditions (pH = 2) in less than 3 h. These findings underscore the importance of both phase and morphology in designing high-performance Bi₂O₃ photocatalysts for environmental remediation. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article