Search Results (7,245)

Conventional optoelectronic synapses rely on electrical signals for core operations, resulting in complex circuitry, limited response speed, and energy inefficiency. Herein, an all-optical synapse based on perovskite MAPbBr₂I is developed that directly converts optical stimuli into transmittance responses that mimic fundamental synaptic plasticity, including paired-pulse facilitation, short- and long-term memory, and learning. By using the dynamic transmittance response as input to an artificial neural network, high-accuracy dynamic pattern recognition of sequential characters is achieved. Furthermore, the optically controlled transmittance states are successfully integrated as programmable weights into a diffractive neural network, enabling all-optical classification of MNIST handwritten digits with an accuracy of 89%. This fully optical architecture, which eliminates electronic components and complex circuits, offers a promising pathway toward high-speed, energy-efficient vision systems by fundamentally circumventing the von Neumann bottleneck. Full article

Synthetic Aperture Radar (SAR) images offer significant advantages for monitoring the dynamics of water bodies in tropical regions, mainly due to their ability to acquire data under adverse weather conditions, which frequently limit optical sensors. However, the automated classification of water bodies using SAR data still faces methodological challenges, particularly regarding the selection of the most suitable parameters and polarizations. This study proposes a multitemporal classification methodology using Sentinel-1 data to map the flood regimes of lagoon complexes in the State of Rio de Janeiro (Brazil). The approach integrates SAR image time series with the Random Forest machine learning algorithm, evaluating the performance of different polarization configurations (VV, VH, and VV–VH). The results show that the combined use of single and cross polarizations (VV–VH) achieved excellent performance, with a Kappa index of 0.83, F-score of 0.90, and overall accuracy of 0.96, demonstrating methodological robustness. The multitemporal analysis identified approximately 294 km² of permanently flooded areas, while seasonally flooded areas, associated with the seasonal variation in coastal lagoons, exhibited variations exceeding 30 km² over the time series. Full article

Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article

Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technique widely used in medical diagnosis, biomedical research and other fields. It plays an important role in the early detection and accurate diagnosis of diseases. The superluminescent light-emitting diode (SLED) is the ideal light source for OCT systems, where the stability of its drive current and operating temperature directly determines the imaging quality of OCT. Existing driving and temperature control schemes for similar light sources predominantly rely on microcontrollers or field programmable gate arrays (FPGAs), a reliance which often results in complex system architectures and difficulties in balancing simplicity with control precision. To address these issues, a stable and compact SLED source driver module designed for OCT was developed in this study, integrating both a constant-current drive circuit and a temperature control circuit. The negative feedback control and improved current-limiting protection are employed in the constant-current drive circuit to maintain stable SLED operation and reduce the circuit footprint. A miniature dedicated temperature control chip is adopted in the temperature control circuit. The operating temperature of the SLED is acquired by linearizing the negative temperature coefficient (NTC) thermistor value and regulated through a proportional-integral-derivative (PID) compensation circuit. The size of the fabricated module (including casing) is less than 10 × 8 × 3 cm³. Experimental results show that the driver module achieves a drive current control accuracy of 0.1% and a temperature control accuracy of 0.01 °C. The output optical power fluctuation is less than 0.005 mW and the average axial resolution for OCT is 6.5992 μm with a standard deviation of 0.0107 μm. This light source driver module successfully balances control precision with structural simplicity, demonstrating excellent applicability in OCT systems. Full article

This study presents a modular and scalable wearable functional near-infrared spectroscopy (fNIRS) system for high-resolution cerebral hemodynamic signal acquisition. The system is based on compact optoelectronic modules and supports mixed measurements using short-separation and long-separation channels, offering good scalability and spatial adaptability. The integrated quartz light guide structure improves optical coupling efficiency between the probe and scalp. A series of in vivo experiments validated system performance. In a forearm arterial occlusion experiment, the system accurately captured concentration changes in oxygenated and deoxygenated hemoglobin during blood flow blockade and reperfusion, with large effect sizes (Cohen’s d > 0.9). In a prefrontal cortex Valsalva experiment, the biphasic response characteristic of neurovascular coupling was successfully resolved. In a 2-back working memory task, the system identified a task-related frequency component (0.0227 Hz) and right-lateralized prefrontal cortex activation (p = 0.023). These results demonstrate that the system exhibits a good signal-to-noise ratio and temporal dynamic response, enabling high-resolution mapping of regional hemodynamic changes. This work provides an effective solution for the development of wearable, modular, and high-precision multi-channel fNIRS systems. Full article

Background: Endoscopic assessment is central to the management of inflammatory bowel disease (IBD), particularly within treat-to-target strategies. However, conventional high-definition white-light endoscopy (HD-WLE) is limited by interobserver variability and its inability to reliably reflect microscopic inflammation or predict long-term outcomes. Over the last decade, multiple technological innovations have reshaped the role of endoscopy in both disease activity monitoring and dysplasia surveillance. Methods: This narrative review provides a comprehensive and clinically oriented overview of emerging endoscopic technologies in IBD, including image-enhanced endoscopy, ultra-high-magnification techniques, artificial intelligence (AI), and molecular imaging. We discuss their diagnostic performance, prognostic implications, and potential integration into clinical practice. Results: Image-enhanced endoscopy improves visualization of subtle mucosal and vascular alterations and demonstrates stronger correlation with histological activity compared with HD-WLE alone. Confocal laser endomicroscopy and endocytoscopy enable in vivo microscopic assessment of epithelial architecture and barrier integrity, redefining remission beyond macroscopic healing. AI systems have shown expert-level performance in grading inflammatory severity in ulcerative colitis and high sensitivity in capsule endoscopy for Crohn’s disease, supporting objective and reproducible assessment. In surveillance, targeted high-definition inspection has replaced random biopsies, while adjunctive optical and AI-based tools enhance lesion detection and characterization. Molecular imaging introduces a predictive dimension by enabling visualization of drug–target engagement and dysplasia-specific pathways. Conclusions: Endoscopy in IBD is evolving from a descriptive modality toward a multimodal precision tool integrating enhanced imaging, AI-driven standardization, and molecular profiling. Although further validation and cost-effectiveness studies are required, these innovations have the potential to improve therapeutic stratification, surveillance strategies, and long-term patient outcomes. Full article

Silicone hydrogels offer high oxygen permeability but suffer from poor wettability. This study integrates a TRISS-based system (0–2.0 wt%) with a fixed PVP/NVP matrix (1.0/0.5 wt%) to enhance hydration-induced dimensional stability and surface properties. Fabricated via cast-molding, the lenses demonstrated that TRISS incorporation significantly enhances oxygen transport. Specifically, the 2.0 wt% TRISS formulation (S2.0) achieved an ~1.9-fold increase in oxygen-induced current (from 0.97 μA in pure-HEMA to 1.86 μA) while strongly suppressing hydration-induced swelling. To counter TRISS’s inherent hydrophobicity, the PVP/NVP matrix acted as a vital compensatory mechanism, driving the equilibrium contact angle down to 56.04° and avoiding the severe hydrophobic plateau (93.79°) of the additive-free comparator. S2.0 maintained a robust oxygen response alongside improved wettability. In conclusion, this system defines a workable low-silicone design window accommodating up to 2.0 wt% TRISS without wettability loss or optical degradation (>97%). Crucially, by leveraging TRISS to mitigate swelling-induced mechanical stress and PVP/NVP to ensure stable wettability, this structurally robust hydrogel provides a highly viable foundational matrix for future smart contact lenses equipped with diagnostic micro-components. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

Many biomedical applications rely on the accurate recovery of absorption and scattering properties of human tissue. These characteristics serve as useful diagnostic indicators, holding information regarding the health and physiological status of a human subject. Many experimental methods exist for the determination of these optical properties, though many, such as integrating sphere methods, are not easily used in an in vivo setting. We have constructed and validated a spatially resolved reflectance imaging system that can be used to measure the absolute optical properties of absorbing turbid media in a non-contact, non-invasive fashion. We present detailed calibration procedures that consider our unique incident beam profile and system response with quantitative comparisons between experimentally and computationally obtained reflectance using Monte Carlo methods. Using highly scattering sphere suspensions with added absorption by ink, we show the spatially resolved reflectance imaging system’s ability to recover absorption within 20% of reference collimated transmission measurements and reduced scatter within 6% of those obtained by an extensively tested integrating sphere system, validating our system in preparation for in vivo measurements of the optical properties of human skin. Full article

The 589 nm three-frequency lidar systems are widely employed for detecting atmospheric parameters in the mesosphere and lower thermosphere (MLT). Recently, the single-peak atomic frequency discriminator (SPAFD) has enabled 589 nm three-frequency lidars to measure wind fields in the stratosphere and mesosphere. However, research on their application for near-surface aerosol measurements remains limited. This paper proposes a method for diurnal aerosol detection using the 589 nm three-frequency lidar integrated with SPAFD. The specific configuration of the lidar system is described in detail, along with the retrieval algorithm for aerosol optical parameters derived from the three-frequency backscatter signals. Continuous 69-h observation results of the aerosol backscatter ratio are provided, followed by an analysis of the diurnal evolution of the planetary boundary layer (PBL) height. This approach enables existing 589 nm lidar systems to acquire aerosol diurnal detection capabilities without additional hardware costs or operational expenses. At present, the retrieval of aerosol extinction coefficients is constrained to altitudes above 10 km due to geometric overlap factor limitations. To overcome this, a dedicated low-altitude detection channel will be integrated in future iterations to enable full-altitude measurements. This advancement will establish the 589 nm lidar as a highly efficient tool for full-altitude, diurnal atmospheric detection. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

The photoelectrochemical splitting (PEC) of water provides a direct route to converting solar energy into storable chemical fuels. When illuminated, a semiconductor photoelectrode can absorb light and generate electron-hole pairs, which participate in interfacial redox reactions at the semiconductor-electrolyte junction. Therefore, to achieve high-performance PEC, photoelectrodes with optimized optical absorption and charge have been explored. This review analyzes recent fabrication strategies used to design photoelectrodes for the PEC dissociation of water. Physical fabrication techniques, including pulsed laser deposition, magnetron sputtering, and physical vapor deposition, allow for precise control of film thickness, crystallinity, and defect density, critical parameters for efficient charge transport. Typically, in physical methods, reported photocurrent densities span from ~10⁻² to 10¹ mAcm⁻², depending on the semiconductor material, nanostructure design, and interfacial engineering strategies. Chemical synthesis methods, such as hydrothermal growth, successive ion layer adsorption and reaction, and microemulsion techniques, provide greater compositional flexibility and enable controlled doping, surface functionalization, and the formation of nanostructured morphologies. Finally, hybrid fabrication strategies integrate physical and chemical processes within a single synthesis framework to combine structural precision with compositional tuning capabilities. These approaches enable the development of advanced architecture such as heterojunctions, core–shell nanostructures, and catalyst-modified interfaces, which enhance light absorption and optimize interfacial transfer. Furthermore, theoretical and computational tools are here analyzed as complementary approaches that guide the rational design and optimization of photoelectrochemical materials and devices. Full article

Simultaneous hydrogen fuel and value-added chemical production from renewable resources is a key strategy in sustainable catalysis. This work presents a novel strategy employing metal–organic frameworks (MOFs) as precursors for synthesizing advanced titanium dioxide (TiO₂) photocatalysts with enhanced structural and optical properties. Two photocatalysts, M-BDC and M-2,5PDC, were synthesized via controlled calcination of MIL-125(Ti) using terephthalic and 2,5-pyridinedicarboxylic acids, respectively. Characterization confirmed the formation of mixed anatase/rutile TiO₂ phases with mesoporous structures. Notably, nitrogen incorporation in M-2,5PDC reduced the optical band gap to 2.94 eV compared with 3.08 eV for M-BDC, enhancing visible-light absorption. Photocatalytic experiments conducted at near-neutral pH (6.0) demonstrated effective simultaneous glycerol oxidation and hydrogen evolution without the use of alkaline additives. M-BDC achieved 30% glycerol conversion with 78.85% selectivity toward dihydroxyacetone and 21.15% toward glyceraldehyde, while M-2,5PDC exhibited selectivities of 71.55% and 28.45%, respectively. Glycerol underwent partial oxidation without complete mineralization, generating high-value products in parallel with hydrogen production. Both catalysts displayed excellent reuse stability across three consecutive cycles, with M-BDC showing enhanced dihydroxyacetone selectivity (78.85% to 84.42% between cycles). This MOF-derived TiO₂ platform integrates controlled synthesis, near-neutral pH operation, high selectivity, and catalytic stability, thereby establishing a viable strategy for the simultaneous production of clean fuel and value-added chemicals from renewable resources. Full article

The on-chip detection of circularly polarized light is pivotal for advancing applications in quantum optics, information processing, and spectroscopic sensing. However, conventional chiral metasurfaces often suffer from complex multilayer fabrication, material incompatibility, or modest performance, hindering their integration with photonic circuits. Here, we introduce a monolithic all-silicon metasurface that overcomes these limitations through a singular structural innovation. By strategically truncating four corners of a conventional Z-shaped meta-atom, we induce a hybridization of optical modes that profoundly enhances chiral light–matter interaction. This deliberately engineered perturbation yields a colossal circular dichroism with an extinction ratio exceeding 66 dB, a performance that surpasses existing state-of-the-art designs by approximately three orders of magnitude. Furthermore, the proposed metasurface exhibits remarkable fabrication robustness, owing to its single-layer architecture and CMOS-compatible material. We demonstrate that this exceptional metasurface can be directly integrated with a Mercury Cadmium Telluride (MCT) photodetector to form a highly efficient, compact circular polarization detector. Our work provides a simple yet powerful paradigm for creating high-performance chiral photonic devices, paving the way for their widespread adoption in integrated optoelectronics. Full article

This study presents an all-optical artificial synapse based on 2D perovskite materials for neuromorphic visual simulation. While conventional optoelectronic synapses, which integrate memory and processing, are prevalent in this field, their inherent optical-to-electrical conversion during signal processing incurs significant energy costs. In contrast, our proposed device operates purely in the optical domain. Under ultraviolet–visible light control, the change in light transmittance of this device can simulate various key biological synaptic plasticity behaviors, including paired-pulse facilitation and learning ability. By integrating these devices into a 28 × 28 synaptic array, we constructed an artificial neural network that mimics the experience-driven enhancement characteristic of human visual perceptual learning. Under light-responsive regulation, the system optimized image recognition learning behavior, and after multiple training sessions, the recognition accuracy stabilized above 97%. This study is based on two-dimensional perovskite materials and provides a new material platform for realizing intelligent visual systems with adaptive learning capabilities. Full article