Search Results (221)

Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km², which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing. Full article

Anti-resonant hollow-core fibers have exhibited excellent performance in applications such as high-power pulse transmission, network communication, space exploration, and precise sensing. Employing anti-resonant hollow-core fibers instead of light guiding arms for transmitting laser energy at the 2.79 μm band can significantly enhance the flexibility of medical laser handles, reduce system complexity, and increase laser transmission efficiency. Nevertheless, common anti-resonant hollow-core fibers do not have the ability to maintain the polarization state of light during laser transmission, which greatly affects their practical applications. In this paper, we propose a polarization-maintaining anti-resonant hollow-core fiber applicable for transmission at the mid-infrared 2.79 μm band. This fiber features a symmetrical geometric structure and an asymmetric refractive index cladding composed of quartz and a type of mid-infrared glass with a higher refractive index. Through optimizing the fiber structure at the wavelength scale, single-polarization transmission can be achieved at the 2.79 μm wavelength, with a polarization extinction ratio exceeding 1.01 × 10⁵, indicating its stable polarization-maintaining performance. Simultaneously, it possesses low-loss transmission characteristics, with the loss in the x-polarized fundamental mode being less than 9.8 × 10⁻³ dB/m at the 2.79 µm wavelength. This polarization-maintaining anti-resonant hollow-core fiber provides a more reliable option for the light guiding system of the 2.79 μm Er; Cr: YSGG laser therapy device. Full article

Metasurfaces have drawn extensive research attention for their unique optical properties and vast application potential. Among the various resonant modes induced in metasurfaces, BIC and electric anapole modes stand out as particularly interesting due to their distinctive physical characteristics. In this work, we designed and investigated novel dimeric dumbbell-shaped metasurfaces incorporating two independently tunable asymmetric parameters. This structural innovation enables the simultaneous excitation of both electric anapole and QBIC modes under normally incident MIR illumination. More importantly, by adjusting these two asymmetric parameters, one can independently tune the resonance peaks of the two modes, thereby overcoming the performance limits of conventional single-peak modulation. This metasurface design demonstrates outstanding performance for dielectric environment-sensing applications. We conducted a comprehensive investigation of the sensing sensitivity for dumbbell-shaped metasurfaces of various geometries. Our simulation results show that the circular-shaped configuration achieved high sensitivity, reaching 20,930 GHz/RIU. This work offers a novel design paradigm for multi-mode control and functionalization of metasurface structures. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

On-chip spectral splitting structures with compact footprints hold tremendous potential for next-generation molecular sensing applications in the mid-infrared region. Here, we propose and theoretically investigate a carefully designed structure comprising a tilt grating and metalenses for dual-band spectral splitting with enhanced bandwidth. The tilt grating serves to separate the wavelength bands, and the metalenses following the grating guarantee a smooth transition of light into single-mode waveguides, giving rise to transmittances of 73.59% at 4 μm and 68.74% at 11 μm. The use of this tandem structure results in a significant footprint reduction and a remarkable 25.8% bandwidth enhancement over conventional approaches. The proposed spectral splitting scheme, with its broad wavelength range applicability, unlocks new pathways for on-chip simultaneous multi-target molecule detection. Full article

We developed a photothermal deflection spectroscopy (PTDS) system for the noninvasive analysis of biological tissue. This system detects heat induced by irradiation with pulse-modulated mid-infrared light as the deflection of a probe laser. The probe light is incident on the sensing element horizontal with respect to its contact surface with the sample. This setup simplifies the optical alignment compared to conventional systems, which require the probe laser to be totally reflected at the prism contact surface and aligned with the point of mid-infrared light irradiation. In this study, we measured the PTDS spectra of biological samples to determine the characteristic features of their infrared absorption. We also compared the measurement reproducibility of two configurations: a horizontal optical path and a total reflection optical path. The horizontal optical path showed greater measurement reproducibility than the total reflection optical path when performing intermittent measurements on the wrist. Full article

This work presents a high-performance novel photodetector based on two-dimensional boron nitride (BN) nanosheets functionalized with gold nanoparticles (Au NPs), offering ultra-broadband photoresponse from 0.25 to 5.9 μm. Operating in both photovoltaic and photoconductive modes, the device features rapid response times (<0.5 ms), high responsivity (up to 1015 mA/W at 250 nm and 2.5 V bias), and thermal stability up to 100 °C. The synthesis process involved CO₂ laser exfoliation of hexagonal boron nitride, followed by gold NP deposition via RF sputtering and thermal annealing. Structural and compositional analyses confirmed the formation of a three-dimensional network of atomically thin BN nanosheets decorated with uniformly distributed gold nanoparticles. This architecture facilitates plasmon-enhanced absorption and efficient charge separation via heterojunction interfaces, significantly boosting photocurrent generation across the deep ultraviolet (DUV), visible, near-infrared (NIR), and mid-infrared (MIR) spectral regions. First-principles calculations support the observed broadband response, confirming bandgap narrowing induced by defects in h-BN and functionalization by gold nanoparticles. The device’s self-driven operation, wide spectral response, and durability under elevated temperatures underscore its strong potential for next-generation broadband, self-powered, and wearable sensing and optoelectronic applications. Full article

Two-dimensional (2D) materials have revolutionized the field of optoelectronics by offering exceptional properties such as atomically thin structures, high carrier mobility, tunable bandgaps, and strong light–matter interactions. These attributes make them ideal candidates for next-generation photodetectors operating across a broad spectral range—from ultraviolet to mid-infrared. This review comprehensively examines the recent progress in 2D material-based photodetectors, highlighting key material classes including graphene, transition metal dichalcogenides (TMDCs), black phosphorus (BP), MXenes, chalcogenides, and carbides. We explore their photodetection mechanisms—such as photovoltaic, photoconductive, photothermoelectric, bolometric, and plasmon-enhanced effects—and discuss their impact on critical performance metrics like responsivity, detectivity, and response time. Emphasis is placed on material integration strategies, heterostructure engineering, and plasmonic enhancements that have enabled improved sensitivity and spectral tunability. The review also addresses the remaining challenges related to environmental stability, scalability, and device architecture. Finally, we outline future directions for the development of high-performance, broadband, and flexible 2D photodetectors for diverse applications in sensing, imaging, and communication technologies. Full article

Thermal imaging technology has revolutionized various fields, but current high operating temperature (HOT) mid-wave infrared (MWIR) cameras, particularly those based on xBn detectors, face limitations in size and cost due to the need for cooling to 150 Kelvin. This study explores the potential of extending the operating temperature of these cameras to 180 Kelvin, leveraging advanced AI algorithms to mitigate the increased thermal noise expected at higher temperatures. This research investigates the feasibility and effectiveness of this approach for remote sensing applications, combining experimental data with cutting-edge image enhancement techniques like Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The findings demonstrate the potential of 180 Kelvin operation for xBn MWIR cameras, particularly in daylight conditions, paving the way for a new generation of more affordable and compact thermal imaging systems. Full article

Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. However, when it comes to molecules with different kinds and concentrations, detection technology and data processing remain a challenging task. In this study, we investigate the qualitative and quantitative detection of two kinds of biomolecules in the mid-infrared region simultaneously by the utilization of a plasmonic sensor. The strong coupling between each plasmonic resonance and the corresponding molecular vibration is found to significantly enhance the absorption signal of molecules, and the obtained Rabi splitting is not only a proof of molecular existence but also an indicator of molecular concentration. However, the amount of the molecular solution with a background refractive index in turn affects the plasmonic resonance position. In more general situations, it is not easy to achieve the match between plasmonic resonance and molecular resonance, and thus the quantitative detection by the Rabi splitting depth is not always feasible. Hence, we propose a machine learning algorithm called principal component analysis (PCA), providing a versatile approach for analyzing the proportion of each molecule in the mixture. Our work opens up new routes in noninvasive optical sensing and the integration of AI-driven data analysis further strengthens its potential for real-world applications. Full article

Background/Objectives: Salivary gland tumors (SGTs) are a rare and histologically heterogeneous group of neoplasms that are challenging to diagnose due to phenotypic heterogeneity and overlapping histomorphological markers. Accurate diagnosis is required for clinical management, particularly in unusual subtypes. The objective of this study was to ascertain whether attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, in combination with enzymatic deglycosylation, would be useful in SGT classification by detecting glycosylation-related metabolic variations. Methods: 155 tissue sections, consisting of 80 SGTs and 75 controls, were analyzed. ATR-FTIR spectroscopy was used to record the mid-infrared (MIR) spectra (4000–400 cm⁻¹) of enzymatically untreated and deglycosylated samples. Spectral data were preprocessed and analyzed by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Enzymatic deglycosylation focused on sialic acid and fucose residues with α2-3,6,8 neuraminidase, α1-2,4,6 fucosidase O, and α1-3,4 fucosidase. Results: Tumor and control samples were discriminated with an OPLS-DA model, achieving an accuracy of 81.9% (78.7% for controls and 85.0% for tumors), especially in the glycosylation-relevant spectral range (850–1250 cm⁻¹). Classification between benign and malignant tumors was more challenging, with an accuracy of 70.0% (72.5% for benign and 67.5% for malignant cases). Enzymatic deglycosylation resulted in detectable changes in the MIR spectra, confirming the contribution of glycosylation to tumor-specific signatures. Benign vs. malignant tumor discrimination was still poor and was not much enhanced in the sense of incorporating glycosylation-specific regions. Conclusions: ATR-FTIR spectroscopy coupled with enzymatic deglycosylation can distinguish tumor and control tissues based on glycan-associated spectral differences. Application of the technique to benign/malignant SGT discrimination is hampered by spectral overlap and tumor heterogeneity. Further research will be necessary to explore other clustering algorithms and larger and more homogeneous datasets for improved diagnostic accuracy. Full article

Mid-wave infrared (MWIR) hyperspectral imaging integrates MWIR technology with hyperspectral remote sensing, enabling the capture of radiative information that is difficult to obtain in the visible spectrum, thus demonstrating significant value in camouflage recognition and stealth design. However, there is a notable lack of open-source datasets and effective classification methods in this field. To address these challenges, this study proposes a dual-channel attention convolutional neural network (DACNet). First, we constructed four MWIR camouflage datasets (GCL, SSCL, CW, and LC) to fill a critical data gap. Second, to address the issues of spectral confusion between camouflaged targets and backgrounds and blurred spatial boundaries, DACNet employs independent spectral and spatial branches to extract deep spectral–spatial features while dynamically weighting these features through channel and spatial attention mechanisms, significantly enhancing target–background differentiation. Our experimental results demonstrate that DACNet achieves an average accuracy (AA) of 99.96%, 99.45%, 100%, and 95.88%; an overall accuracy (OA) of 99.94%, 99.52%, 100%, and 96.39%; and Kappa coefficients of 99.91%, 99.41%, 100%, and 95.21% across the four datasets. The classification results exhibit sharp edges and minimal noise, outperforming five deep learning methods and three machine learning approaches. Additional generalization experiments on public datasets further validate DACNet’s superiority in providing an efficient and novel approach for hyperspectral camouflage data classification. Full article

Colloidal quantum dots (QDs) have emerged as promising materials for the development of infrared photodetectors owing to their tunable band gaps, cost-effective manufacturing, and ease of processing. This paper provides a comprehensive overview of the fundamental properties of quantum dots and the operating principles of various infrared detectors. We review the latest advancements in short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) detectors employing colloidal quantum dots. Despite their potential, these detectors face significant challenges compared to conventional infrared technologies. Current commercial applications are predominantly limited to the near-infrared and short-wave bands, with medium- and long-wave applications still under development. The focus has largely been on lead and mercury-based quantum dots, which pose environmental concerns, underscoring the need for high-performance, non-toxic materials. Looking forward, the development of large array and small pixel detectors and improving compatibility with readout circuits are critical for future progress. This paper discusses these hurdles and offers insight into potential strategies to overcome them, paving the way for next-generation infrared sensing technologies. Full article

Circularly polarized light (CPL) detection sensors have significant potential for applications in quantum communication and biosensing. In this work, we propose a three-layer complementary chiral metasurface (TCCM) for on-chip integration in the mid-infrared range (2–6 μm). The TCCM consists of an Al nanorod layer, a SiO₂ dielectric layer, and an Al nanoslit layer, with strong circular dichroism (CD) achieved through the symmetry breaking of the inclined rectangular rods. Finite-difference time-domain (FDTD) simulation results demonstrate that the electric fields excited by left circularly polarized (LCP) light and right circularly polarized (RCP) light exhibit different bonding and antibonding modes, which explains the CD mechanism. The CD response and spectral tunability are influenced by the angle and length of the inclined rectangular rods. Through simulation optimization of structural parameters, a maximum CD value of 0.72 is achieved. Compared to traditional multilayer chiral metasurfaces, the TCCM simplifies the fabrication process. These findings provide valuable insights and practical strategies for the development of compact infrared devices, particularly in optical communication, chiral sensing, and full-Stokes polarization detection. Full article