Search Results (7,835)

Nanomaterials can significantly improve the analytical performance of optical sensors for environmental pollutants. Two-dimensional (2D) molybdenum sulfide (MoS₂) exhibits some unique physicochemical properties, such as strong light–matter interactions, bandgap tunability, and high carrier mobility, which are beneficial for constructing flexible optoelectronic devices. In this review, the principle and classification of 2D MoS₂-based photodetectors (PDs) are introduced, followed by a discussion about the physicochemical properties of 2D MoS₂, as well as the structure–property relationships of 2D MoS₂-based photoactive materials for PDs to understand the modulation strategies for enhancing the photodetection performance. Furthermore, we discuss significant advances in the surface modification and functionalization of 2D MoS₂ for developing high-performance PDs, particularly focusing on synthesis pathways, modification strategies, and underlying physiochemical mechanisms for enhanced photodetection capabilities. Finally, conclusions and research perspectives on resolving significant bottlenecks or remaining challenges are offered based on recent developments in 2D MoS₂-based PDs. Full article

The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may contribute to dermatological care disparities in patients with darker skin phototypes, including the misdiagnosis of wound healing progression and escalated dermatological disease severity. This study introduces (1) an optical sensor measuring reflected light across 410–940 nm, (2) an unsupervised K-means algorithm for skin phototype classification using broadband optical data, and (3) methods to optimize classification across the Near-ultraviolet-A, Visible, and Near-infrared spectra. The differentiation capability of the algorithm was compared to human assessment based on FSPC in a diverse participant population (n = 30) spanning an even distribution of the full FSPC scale. The FSPC assessment distinguished between light and dark skin phototypes (e.g., FSPC I vs. VI) at 560, 585, and 645 nm but struggled with more similar phototypes (e.g., I vs. II). The K-means algorithm demonstrated stronger differentiation across a broader range of wavelengths, resulting in better classification resolution and supporting its use as a quantifiable and reproducible method for skin type classification. We also demonstrate the optimization of this method for specific bandwidths of interest and their associated clinical implications. Full article

In recent decades, nanoscience has experienced rapid global advancements due to its focus on materials and compounds at the nanoscale with high efficiency across diverse applications. WO₃ nanostructures have proven to be a unique material in the development of new technologies due to their electrical, optical, and catalytic properties. They have been used as raw materials for the fabrication of electrochromic devices, optoelectronic devices, hydrogenation catalysts, gas sensors, adsorbents, lithium-ion batteries, solar driven-catalysts, and phototherapy. One of the most striking characteristics of WO₃ is its morphological diversity, spanning from 0D to 2D, encompassing nanoparticles, nanowires, nanofibers, nanorods, nanosheets, and nanoplates. This review discusses common synthesis methods for WO₃ nanostructures, including hydrothermal and solvothermal methods, microwave-assisted synthesis, sol-gel, electrospinning, co-precipitation, and solution combustion, with emphasis on the advantages and challenges of each of them. The processes involved, the obtained morphologies, and the resulting applications are also presented. As evidenced here, the fine control of the synthesis parameters allows the production of nanostructures with controlled phase, morphology, and size, essential aspects for the production of high-performance WO₃-based devices. Full article

A new instrument for label-free measurements based on optical Low-Q Whispering Gallery Modes (WGMs) for various applications is used for a detailed study of the deposition and release of Layer-by-Layer polymer coatings. The two selected coating pairs interact either via hydrogen bonding or electrostatic interactions. Their assembly was followed by common Quartz Crystal Microbalance (QCM) technology and the Low-Q WGMs. In contrast to planar QCM sensor chips of 1 cm, the WGM sensors are fluorescent spherical beads with diameters of 10.2 µm, enabling the detection of analyte quantities in the femtogram range in tiny volumes. The beads, with a very smooth surface and high refractive index, act as resonators for circular light waves that can revolve up to 10,000 times within the bead. The WGM frequencies are highly sensitive to changes in particle diameter and the refractive index of the surrounding medium. Hence, the adsorption of molecules shifts the resonance frequency, which is detected by a robust instrument with a high-resolution spectrometer. The results demonstrate the high potential of the new photonic measurement and its advantages over QCM technology, such as cheap sensors (billions in one Eppendorf tube), simple pre-functionalization, much higher statistic safety by hundreds of sensors for one measurement, 5–10 times faster analysis, and that approx. 25, 000 fewer analyte molecules are needed for one sensor. In addition, the deposited molecule amount is not superposed by hydrated water as for QCM. A connection between sensors and instruments does not exist, enabling application in any transparent environment, like microfluidics, drop-on slides, Petri dishes, well plates, cell culture vasculature, etc. Full article

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring. Full article

The Meteorological Optical Range (MOR) is a measurement of atmospheric visibility. Visibility impairment has been linked to increased aerosol levels in the air. This study conducted statistical analyses using meteorological, air pollutant concentration, and MOR data collected in Mexico City from August 2014 to December 2015 to determine the factors contributing to haze occurrence (periods when MOR < 10,000 m), defined using a light scatter sensor (PWS100). The outcomes revealed seasonal patterns in PM_2.5 and relative humidity (RH) for haze occurrence along the year. PM_2.5 levels during hazy periods in the dry season were higher compared to the wet season, aligning with periods of poor air quality (PM_2.5 > 45 μg/m³). Pollutant-to-CO ratios suggested that secondary aerosols’ production, led by SO₂ conversion to sulfate particles, mainly impacts haze occurrence during the dry season. Meanwhile, during the rainy season, the PWS100 registered haze events even with PM_2.5 values close to 15 μg/m³ (considered good air quality). The broadened distribution of extinction efficiency during the wet period and its correlation with RH suggest that aerosol water vapor uptake significantly impacts visibility during this season. Therefore, attributing poor visibility strictly to poor air quality may not be appropriate for all times and locations. Full article

Gold nanoparticles (AuNPs) are highly attractive for applications in the field of biosensing, particularly for colorimetric nucleic acid detection. Their unique optical properties, which are highly sensitive to changes in their environment, make them ideal candidates for developing simple, rapid, and cost-effective assays. When functionalized with oligonucleotides (Au-nanoprobes), they can undergo aggregation or dispersion in the presence of complementary sequences, leading to distinct color changes that serve as a visual signal for detection. Aggregation-based assays offer significant advantages over other homogeneous assays, such as fluorescence-based methods, namely, label-free protocols, rapid interactions in homogeneous solutions, and detection by the naked eye or using low-cost instruments. Despite promising results, the application of Au-nanoprobe-based colorimetric assays in complex biological matrices faces several challenges. The most significant are related to the colloidal stability and oligonucleotide functionalization of the Au-nanoprobes but also to the mode of detection. The type of functionalization method, type of spacer, the oligo–AuNPs ratio, changes in pH, temperature, or ionic strength influence the Au-nanoprobe colloidal stability and thus the performance of the assay. This review elucidates characteristics of the Au-nanoprobes that are determined for colorimetric gold nanoparticles (AuNPs)-based nucleic acid detection, and how they influence the sensitivity and specificity of the colorimetric assay. These characteristics of the assay are fundamental to developing low-cost, robust biomedical sensors that perform effectively in biological fluids. Full article

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. Full article

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of ${\mathrm{PM}}_1$ and ${\mathrm{PM}}_{2.5}$ as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the ${\mathrm{PM}}_1$ calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the ${\mathrm{PM}}_{2.5}$ model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. Full article

Nanotubular hafnia arrays hold significant promise for advanced opto- and nanoelectronic applications. However, the known studies concern mostly the luminescent properties of doped HfO₂-based nanostructures, while the optical properties of nominally pure hafnia with optically active centers of intrinsic origin are far from being sufficiently investigated. In this work, for the first time we have conducted research on the wide-range temperature effects in the photoluminescence processes of anion-defective hafnia nanotubes with an amorphous and monoclinic structure, synthesized by the electrochemical oxidation method. It is shown that the spectral parameters, such as the position of the maximum and half-width of the band, remain almost unchanged in the range of 7–296 K. The experimental data obtained for the photoluminescence temperature quenching are quantitatively analyzed under the assumption made for two independent channels of non-radiative relaxation of excitations with calculating the appropriate energies of activation barriers—9 and 39 meV for amorphous hafnia nanotubes, 15 and 141 meV for monoclinic ones. The similar temperature behavior of photoluminescence spectra indicates close values of short-range order parameters in the local atomic surrounding of the active emission centers in hafnium dioxide with amorphous and monoclinic structure. Anion vacancies $V_O^{-}$ and $V_O^{2-}$ appeared in the positions of three-coordinated oxygen and could be the main contributors to the spectral features of emission response and observed thermally stimulated processes. The recognized and clarified mechanisms occurring during thermal quenching of photoluminescence could be useful for the development of light-emitting devices and thermo-optical sensors with functional media based on oxygen-deficient hafnia nanotubes. Full article

The detection of heavy metal ions and organic pollutants from water sources remains critical challenges due to their detrimental effects on human health and the environment. Herein, a nitrogen and sulfur co-doped carbon quantum dot (NS-CQDs) fluorescent sensor was developed using a microwave-assisted carbonization method for the detection of Fe³⁺ ions and hydroquinone (HQ) in aqueous solutions. NS-CQDs exhibit excellent optical properties, enabling sensitive detection of Fe³⁺ and HQ, with detection limits as low as 3.40 and 0.96 μM. Notably, with the alternating introduction of Fe³⁺ and HQ, NS-CQDs exhibit significant fluorescence (FL) quenching and recovery properties. Based on this property, a reliable “on-off-on” detection mechanism was established, enabling continuous and reversible detection of Fe³⁺ and HQ. Furthermore, the low cytotoxicity of NS-CQDs was confirmed through successful imaging of HeLa cells, indicating their potential for real-time intracellular detection of Fe³⁺ and HQ. This work not only provides a green and rapid synthesis strategy for CQDs but also highlights their versatility as fluorescent probes for environmental monitoring and bioimaging applications. Full article

Photonic therapy is emerging as a promising method in neuroscience for addressing Alzheimer’s disease (AD). This study uses computational simulations to investigate the impact of specific wavelengths emitted by photodiodes on the light absorption rates in brain tissue for brain implant sensors. Additionally, it presents a novel methodology that enhances light absorption via multi-parameter optimization. By adjusting the angle and wavelength of the incident light, the absorption rate was significantly enhanced using four photodiodes, each emitting at 660 nm with a power input of 3 mW. Notably, an incident angle of 20 degrees optimized light absorption and minimized thermal effects on brain tissue. The findings indicate that photodiodes within the near-infrared spectrum are suitable for low-temperature therapeutic applications in brain tissues, affirming the viability of non-invasive and safe photonic therapy. This research contributes foundational data for advancing brain implant photonic sensor design and therapeutic strategies. Furthermore, it establishes conditions for achieving high light absorption rates with minimal heat generation, identifying optimal parameters for efficient energy transfer. Full article

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10–200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy. Full article