Search Results (184)

Bismuth vanadate (BiVO₄) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic limitations, including poor charge carrier mobility, short diffusion length, and sluggish oxygen evolution reaction (OER) kinetics. This review critically summarizes recent advancements aimed at enhancing BiVO₄ PEC performance, encompassing synthesis strategies, defect engineering, heterojunction formation, cocatalyst integration, light-harvesting optimization, and stability improvements. Key fabrication methods—such as solution-based, vapor-phase, and electrochemical approaches—along with targeted modifications, including metal/nonmetal doping, surface passivation, and incorporation of electron transport layers, are discussed. Emphasis is placed on strategies to improve light absorption, charge separation efficiency (η_sep), and charge transfer efficiency (η_trans) through bandgap engineering, optical structure design, and catalytic interface optimization. Approaches to enhance stability via protective overlayers and electrolyte tuning are also reviewed, alongside emerging applications of BiVO₄ in tandem PEC systems and selective solar-driven production of value-added chemicals, such as H₂O₂. Finally, critical challenges, including the scale-up of electrode fabrication and the elucidation of fundamental reaction mechanisms, are highlighted, providing perspectives for bridging the gap between laboratory performance and practical implementation. Full article

Tooth whitening is increasingly sought in both clinical and home settings, raising concerns about the efficacy and safety of various whitening agents and their delivery systems. This narrative review compares the whitening performance and biocompatibility of active ingredients, including hydrogen peroxide, carbamide peroxide, activated charcoal, sodium bicarbonate, fluoride compounds, and blue covarine, with particular emphasis on the role of polymer-based carriers in formulation strategies. Hydrogen peroxide and carbamide peroxide remain the most effective agents for intrinsic whitening, but are associated with risks of enamel surface alterations, microhardness reduction, and potential cytotoxicity, particularly at higher concentrations. Sodium bicarbonate provides moderate whitening effects through extrinsic stain removal, while fluoride compounds play a supportive role by reducing demineralization and tooth sensitivity, thereby preserving enamel integrity. These properties make them valuable adjuncts or alternatives for patients with high sensitivity risks. Blue covarine offers immediate optical effects without inducing intrinsic color changes, whereas activated charcoal poses risks of enamel abrasion and surface roughness with limited long-term efficacy. Polymer-based carriers such as Carbopol gels, polyvinylpyrrolidone, and hydroxypropyl methylcellulose are incorporated into whitening formulations to improve viscosity, adhesion, and modulate the release of active ingredients. These polymers might help minimize diffusion of bleaching agents into deeper dental tissues, potentially reducing cytotoxic effects, and may improve handling characteristics. However, dedicated studies evaluating the unique advantages of polymers in different whitening systems remain limited. A comprehensive understanding of both the active ingredients and delivery technologies is critical to balancing esthetic outcomes with long-term oral health. From a clinical perspective, polymer-based carriers might contribute to reducing whitening-related tooth sensitivity, improving patient comfort, and providing more predictable treatment outcomes. Continued research is needed to clarify optimal formulations and application protocols, ensuring safer and more effective tooth-whitening practices in both clinical and home-use scenarios. Full article

Synthetic aperture radar (SAR), with its all-weather and all-day active imaging capability, has become indispensable for geoscientific analysis and socio-economic applications. Despite advances in deep learning–based object detection, the rapid and accurate detection of small objects in SAR imagery remains a major challenge due to their extremely limited pixel representation, blurred boundaries in dense distributions, and the imbalance of positive–negative samples during training. Recently, vision–language models such as Contrastive Language-Image Pre-Training (CLIP) have attracted widespread research interest for their powerful cross-modal semantic modeling capabilities. Nevertheless, their potential to guide precise localization and detection of small objects in SAR imagery has not yet been fully exploited. To overcome these limitations, we propose the CLIP-Driven Adaptive Tiny Object Detection Diffusion Network (CDATOD-Diff). This framework introduces a CLIP image–text encoding-guided dynamic sampling strategy that leverages cross-modal semantic priors to alleviate the scarcity of effective positive samples. Furthermore, a generative diffusion-based module reformulates the sampling process through iterative denoising, enhancing contextual awareness. To address regression instability, we design a Balanced Corner–IoU (BC-IoU) loss, which decouples corner localization from scale variation and reduces sensitivity to minor positional errors, thereby stabilizing bounding box predictions. Extensive experiments conducted on multiple SAR and optical remote sensing datasets demonstrate that CDATOD-Diff achieves state-of-the-art performance, delivering significant improvements in detection robustness and localization accuracy under challenging small-object scenarios with complex backgrounds and dense distributions. Full article

In this study, cerium-based metal–organic frameworks (MOFs), cerium terephthalate (CeTPA), were synthesized and incorporated into nanofibers via electrospinning using poly(methyl methacrylate) (PMMA). The synthesized materials were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), diffuse reflectance spectroscopy (DRS), and Tauc plot analysis. The electrospun CeTPA nanofibers exhibited superhydrophobic properties, with water contact angles exceeding 150°. The adsorption and catalytic performance of the nanofibers were assessed for dye removal using Congo red (CR) and methylene blue (MB) as model organic pollutants. Adsorption studies demonstrated negligible dye uptake due to the hydrophobicity of the fibers, while catalytic degradation experiments in the presence of hydrogen peroxide (H₂O₂) showed significant degradation of CR but limited effectiveness against MB, offering high selectivity toward anionic dyes. Structural and optical characterizations confirmed the stability and catalytic activity of CeTPA nanofibers, highlighting their potential for selective dye degradation in wastewater treatment applications. Full article

Optical brain monitoring techniques, including near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and photoplethysmography (PPG) have gained attention for their non-invasive, affordable, and portable nature. These methods offer real-time insights into cerebral parameters like cerebral blood flow (CBF), intracranial pressure (ICP), and oxygenation. However, confounding factors like extracerebral layers, skin pigmentation, skull thickness, and brain-related pathologies may affect measurement accuracy. This review examines the potential impact of confounders, focusing on in vitro studies that use phantoms to simulate human head properties under controlled conditions. A systematic search identified six studies on extracerebral layers, two on skin pigmentation, two on skull thickness, and four on brain pathologies. While variation in phantom designs and optical devices limits comparability, findings suggest that the extracerebral layer and skull thickness influence measurement accuracy, and skin pigmentation introduces bias. Pathologies like oedema and haematomas affect the optical signal, though their influence on parameter estimation remains inconclusive. This review highlights limitations in current research and identifies areas for future investigation, including the need for improved brain phantoms capable of simulating pulsatile signals to assess the impact of confounders on PPG systems, given the growing interest in PPG-based cerebral monitoring. Addressing these challenges will improve the reliability of optical monitoring technologies. Full article

Extreme ultraviolet (EUV) photomask inspection is a critical step in semiconductor manufacturing, requiring high-resolution, high-throughput solutions to detect nanometer-scale defects. Traditional actinic imaging systems relying on complex optics have a high cost of ownership and require frequent upgrades. An alternative is lensless imaging techniques based on ptychography, which offer high-fidelity reconstruction but suffer from slow throughput and high data demands. In particular, the ptychographic standard solver—the iterative Difference Map (DifMap) algorithm—requires many measurements and iterations to converge. We propose Ptycho-LDM, a hybrid framework integrating DifMap with a conditional Latent Diffusion Model for rapid and accurate phase retrieval. Ptycho-LDM alleviates high data acquisition demand by leveraging data-driven priors while offering improved computational efficiency. Our method performs coarse object retrieval using a resource-constrained reconstruction from DifMap and refines the result using a learned prior over photomask patterns. This prior enables high-fidelity reconstructions even in measurement-limited regimes where DifMap alone fails to converge. Experiments on actinic patterned mask inspection (APMI) show that Ptycho-LDM recovers fine structure and defect details with far fewer probe positions, surpassing the DifMap in accuracy and speed. Furthermore, evaluations on both noisy synthetic data and real APMI measurements confirm the robustness and effectiveness of Ptycho-LDM across practical scenarios. By combining generative modeling with physics-based constraints, Ptycho-LDM offers a promising scalable, high-throughput solution for next-generation photomask inspection. Full article

Accurate light simulations using virtual plant models are essential for analyzing how plant structures influence the micro-light climate within canopies. Such simulations are increasingly important in applications including remote sensing, greenhouse optimization, and synthetic data generation for agricultural systems. However, many current models simplify leaf optical behavior by assuming purely diffuse reflectance, thereby neglecting the spectral and angular variability described by the bidirectional reflectance distribution function (BRDF). To address this limitation, the spectral BRDF of cucumber leaves was experimentally measured and corresponding Phong reflectance model parameters were determined for use in the GroIMP simulation environment. These parameters were optimized to replicate the angular and spectral reflectance distribution patterns and evaluated against a diffuse reflectance model. The Phong model successfully reproduced key features of the BRDF, particularly the increased diffuseness in the green and far-red spectral regions, although deviations in hemispherical reflectance emerged at high incidence angles. The resulting Phong parameters offer a practical method for incorporating wavelength- and direction-dependent reflectance into virtual plant simulations. These parameters can be adapted to other reflectance values of leaves with similar optical properties using hemispherical reflectance measurements, enabling more realistic light modeling in virtual canopies. Within a 30–60° incidence, the Phong BRDF reduced per-wavelength error relative to a diffuse baseline across all spectral regions. Full article

W/Si multilayer mirrors are a promising candidate for soft X-ray applications at wavelengths below 2.4 nm. However, their optical performance is strongly affected by interface roughness and interlayer mixing, which limits reflectivity. One approach to improving interface quality is the application of BIAS voltage during deposition. In this study, W/Si multilayer mirrors with bilayer thickness of ~1.5 nm and 100 bilayers were fabricated using DC magnetron sputtering, with ion assistance of 75 V, 100 V, and 200 V applied during the deposition of silicon layers. Grazing incidence X-ray reflectivity (GIXR) measurements at Cu Kα (λ = 0.154 nm) showed that applying BIAS ≤ 100 V reduced interface roughness and increased reflectivity, with a maximum effect observed at 75 V. In contrast, at 200 V, strong diffusion intermixing reduced the bilayer thickness to 1.29 nm and nearly eliminated reflectivity. Soft X-ray reflectivity measurements at λ ~ 1.5 nm confirmed that ion assistance improved optical performance, increasing mirror reflectivity from ~1% (BIAS = 0 V) to ~2.3% (BIAS = 75 V). Atomic force microscopy (AFM) measurements also demonstrated a reduction in surface roughness from 0.22 nm to 0.11 nm due to using ion assistance. These results indicate that moderate ion assistance (<100 V) can enhance the optical quality of W/Si multilayer mirrors by reducing interface roughness, while excessive BIAS (>100 V) leads to diffusion intermixing and optical degradation. The novelty of this work lies in the direct application and variation in BIAS voltage during Si-layer growth, enabling detailed investigation of its influence on interface roughness and reflectivity. This approach provides a simple and effective tool for optimizing the performance of W/Si multilayer mirrors for soft X-ray applications. Full article

There has been considerable scientific interest in third-order nonlinear optical materials for photonic applications. In particular, materials exhibiting a strong electronic optical Kerr effect serve as essential components in the ultrafast nonlinear photonic devices and are instrumental in the development of all-optical signal processing technologies. Therefore, the accurate prediction of material-relevant properties, such as second hyperpolarizabilities, remains a key topic in the search for efficient photonic materials. However, the field standards in quantum chemical computation are still inconsistent, as studies often lack a firm statistical foundation. This work presents a comprehensive in silico investigation based on multiple full-factorial experiments, aiming to clarify the strengths and limitations of various computational approaches. Our results indicate that the coupled-cluster approach at the CCSD level in its current response-equation implementations is not yet able to outperform the range-separated hybrid density functionals, such as LC-BLYP(0.33). The exceptional performance of the specifically tailored basis set Sadlej-pVTZ is also described. Not only was the presence of diffuse functions found to be mandatory, but also adding ample polarization functions is shown to be inefficient resource-wise. HF/Sadlej-pVTZ is proven to be reliable enough to use in molecular screening. Meta functionals are confirmed to produce poorly consistent results, and specific guidelines for constructing range-separated functionals for polarizability calculations are drawn out. Additionally, it was shown that many of the contemporary solvation models exhibit significant limitations in accurately capturing nonlinear optical properties. Therefore, further refinement in the current methods is pending. This extends to the statistical description as well: the mean absolute deviation descriptor is found to be deficient in rating various computational methods and should rather be replaced with the parameters of the linear correlation (the slope, the intercept, and the R²). Full article

Soybean (Glycine max (L.) Merrill) is highly sensitive to water deficit, particularly during the vegetative phase, when morphological and metabolic plasticity support continued growth and photosynthetic efficiency. We applied eleven water regimes, from full irrigation (W100) to total water withholding (W0), to plants grown under controlled conditions. After 14 days, we quantified morphophysiological, biochemical, leaf optical, gas exchange, and chlorophyll a fluorescence traits. Drought induces significant reductions in leaf area, biomass, pigment pools, and photosynthetic rates (A, g_s, ΦPSII) while increasing the levels of oxidative stress markers (electrolyte leakage, ROS) and proline accumulation. OJIP transients and JIP test metrics revealed reduced electron-transport efficiency and increased energy dissipation for many parameters under severe stress. Principal component analysis (PCA) clearly separated those treatments. PC1 captured growth and water status variation, whereas PC2 reflected photoprotective adjustments. These data show that progressive drought limits carbon assimilation via coordinated diffusive and biochemical constraints and that the accumulation of proline, phenolics, and lignin is associated with osmotic adjustment, antioxidant buffering, and cell wall reinforcement under stress. The combined use of hyperspectral sensors, gas exchange, chlorophyll fluorescence, and multivariate analyses for phenotyping offers a rapid, nondestructive diagnostic tool for assessing drought severity and the possibility of selecting drought-resistant genotypes and phenotypes in a changing stress environment. Full article

In low-light optical imaging, the scarcity of incident photons and the inherent limitations of imaging sensors lead to challenges such as low signal-to-noise ratio, limited dynamic range, and degraded contrast, severely compromising image quality and optical information integrity. To address these challenges, we propose a novel low-light image enhancement technique, LightenResDiff, which combines a residual diffusion model with the discrete wavelet transform. The core innovation of LightenResDiff lies in it accurately restoring the low-frequency components of an image through the residual diffusion model, effectively capturing and reconstructing its fundamental structure, contours, and global features. Additionally, the dual cross-coefficients recovery module (DCRM) is designed to process high-frequency components, enhancing fine details and local contrast. Moreover, the perturbation compensation module (PCM) mitigates noise sources specific to low-light optical environments, such as dark current noise and readout noise, significantly improving overall image fidelity. Experimental results across four widely-used benchmark datasets demonstrate that LightenResDiff outperforms existing methods both qualitatively and quantitatively, surpassing the current state-of-the-art techniques. Full article

Distributed acoustic sensing (DAS), which can provide dense spatial and temporal measurements using optical fibers, is quickly becoming critical for large-scale infrastructure monitoring. However, anomaly detection in DAS data is still challenging owing to the spatial correlations between sensing channels and nonlinear temporal dynamics. Recent approaches often disregard the explicit sensor layout and instead handle DAS data as two-dimensional images or flattened sequences, eliminating the spatial topology. This work proposes GraphDiffusion, a novel generative anomaly-detection model that combines a conditional denoising diffusion probabilistic model (DDPM) and a graph neural network (GNN) to overcome these limitations. By treating each channel as a graph node and building edges based on Euclidean proximity, the GNN explicitly models the spatial arrangement of DAS sensors, allowing the network to capture local interchannel dependencies. The conditional DDPM uses iterative denoising to model the temporal dynamics of standard signals, enabling the system to detect deviations without the need for anomalies. The performance evaluations based on real-world DAS datasets reveal that GraphDiffusion achieves 98.2% and 98.0% based on the area under the curve (AUC) of the F1-score at K different levels (F1_K-AUC), an AUC of receiver operating characteristic (ROC) at K different levels (ROC_K-AUC), outperforming other comparative models. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

This study investigates the thermal stability and microstructural evolution of the solid electrolyte medium used in DLyte^® dry electropolishing and dry anodizing processes. Samples were thermally aged between 30 °C and 45 °C to simulate Joule heating during industrial operation. Visual and SEM analyses revealed shape deformation and microcrack formation at temperatures above 40 °C, potentially reducing particle packing efficiency and electrolyte performance. Particle size distribution shifted from bimodal to trimodal upon aging, with an overall size reduction of up to 39.5% due to dehydration effects, impacting ionic transport properties. Weight-loss measurements indicated a diffusion-limited dehydration mechanism, stabilizing at 15–16% mass loss. Fourier transform infrared analysis confirmed water removal while maintaining the essential sulfonic acid groups responsible for ionic conductivity. In dry anodizing tests on titanium, aged electrolytes enhanced process efficiency, producing TiO₂ films with improved optical properties—color and brightness—while preserving thickness and uniformity (~70 nm). The results highlight the need to carefully control thermal exposure to maintain electrolyte integrity and ensure consistent process performance. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article