To miniaturize a microplasma-based optical emission spectrometer with high performance, the spatial distribution of electron density and the spectral responses in a point-discharge (PD) microplasma were systematically studied. COMSOL simulations and simultaneous detection of atomic emission and absorption were designed for spectral characterization. The discharge region around the electrode tips exhibited the highest electron density and spectral intensity. Based on this, a critical excitation source was designed by sequentially arranging three PDs in a discharge chamber. They were symmetrically distributed and axially rotated 60° between each other, forming an axially staggered-trap microplasma source to improve excitation capability and efficiency. This design exposed all of the electrode tips and enlarged the discharge region along the sample transportation/excitation and spectral acquisition direction, forming a microplasma trap. Therefore, it could intercept most analytes and facilitate spatially uniform excitation and unobstructed acquisition of spectral signals from all the electrode tips. Besides, the array configuration enabled tandem excitation to further improve sensitivity. Through coupling with hydride generation for sample introduction, limits of detection of 0.4, 0.1, 0.03, 0.2, 0.1, 1, and 0.05 μg L-1 were achieved for As, Ge, Hg, Pb, Sb, Se, and Sn, respectively, with relative standard deviations below 3% (n = 5). Compared with a conventional single PD, the analytical sensitivities were improved by 5-7 times, and additionally by 8-17 times with more (six) PDs. The accuracy and applicability were demonstrated by analyzing certified reference materials and real samples. It features compactness, low power consumption, and excellent performance, thus showing great promise for on-site elemental analysis.

Gate-controlled electron trap dynamics are quantitatively investigated to elucidate their influence on the spectral and temporal characteristics in MoS2-WSe2 heterostructure photodetector. Defect-induced trap states within the bandgap enable sub-bandgap photon absorption, thereby extending the photodetection range up to 1650 nm. As the gate voltage increases, the Fermi level shifts toward the conduction band, which raises the probability of electron trap occupation and consequently suppresses absorption in certain wavelength ranges. Moreover, increased trap occupancy significantly shortens the photocurrent rising time from 480 ms at -40 V to 1.3 ms at +40 V, whereas the falling time becomes longer because of the reduced trap-assisted recombination. These results indicate that both spectral responsivity and photoresponse time can be dynamically modulated by the gate bias, allowing the device to function as a tunable photodetector that adapts its detection mode to specific application requirements. This study provides fundamental insight into trap-assisted photodetection mechanisms and establishes a platform for future research on gate-controlled optoelectronic devices.

Abstract Multispectral and hyperspectral imaging have been extensively applied in various imaging domains, where spectral channels with narrow bandwidths provide detailed information for optical signal analysis. The integration of multi-channel filter arrays with image sensors is essential for multispectral detection. To extend this capability to cameras without integrated filters, a dual-band spectral filter array (DSFA) combined with a telecentric lens was employed with a monochrome camera for real-time surface plasmon resonance imaging (SPRi). Placement of the DSFA in front of a broadband light source generated spatially modulated excitation signals incident on a gold-coated periodic silicon nanostructure serving as a surface plasmon resonance (SPR) chip. A pixel-shift-based demosaicing method enabled the separation of checkerboard-like images into two spectral bands corresponding to the filters of the DSFA, facilitating γ -based spectral contrast response analysis. This optical configuration successfully demonstrated dynamic monitoring of the interaction between anti-BSA and immobilized BSA on the chip. Compared with wavelength-shift analysis, γ -based analysis improved the refractive index detection limit by nearly two orders of magnitude, enabling highly sensitive monitoring of biomolecular interactions. The DSFA-based SPRi platform provides a flexible, highly integrable, and label-free solution for quantitative analysis of biomolecular interactions.

Abstract In this work, we have demonstrated bias-tunable visible-near infrared spectral response photodetection achieved from p-GaAs/n-graded low-doped AlGaAs heterojunctions. The designed heterojunctions consist of a p-n junction composed of highly doped p-GaAs and n-type low-doped AlxGa1-xAs alloys with various Al contents, such as 0.5, 0.3, and 0 in sequence, a highly doped n-AlGaAs compensation layer, and a high Al content n-Al0.5Ga0.5As barrier layer. Separate absorption of incident photons with different energies has been observed at various interfaces constructed from AlxGa1-xAs with different Al contents. Besides, an effective tunability of the space charge region (SCR) was realized, benefiting from the compensation layer that localized the SCR at the p-GaAs/n-Al0.5Ga0.5As interface under equilibrium states, resulting in controlled collection of excess carriers by simply applying external bias. In addition, the Al0.5Ga0.5As barrier layer has improved the performance of photodetection by enhancing the electron collection efficiency. Collectively, bias-tunable response photodetection in ~ 430-875 nm visible-near infrared spectral range has been achieved, with responsivity and detectivity as high as 0.24 A/W and 1013 Jones, respectively. The results shown herein provide promising pathways for achieving efficient and tunable spectral response photodetection in the future.

Abstract Artificial neural networks underpin modern artificial intelligence but face challenges of scalability, energy consumption, and hardware efficiency as model sizes grow. Photonic approaches offer an attractive alternative by exploiting the parallelism and low thermal footprint of light, yet many implementations still require complex device fabrication or engineered nonlinearities. Extreme learning machines (ELMs) simplify this paradigm by fixing the input‐to‐hidden mapping and training only a linear output layer, making them highly compatible with physical realizations. Here, a photonic ELM (PELM) framework is introduced based on ultrafast transient absorption (TA) spectroscopy, a widely adopted pump–probe technique operating intrinsically on the femtosecond–picosecond timescale. In this system, inputs are encoded through multiple probe‐polarization channels, each parameterized by pump–probe delay, and the resulting TA spectral responses provide high‐dimensional nonlinear features without pixelated modulators or nanofabrication. Using a quasi‐1D ZrSe 3 nanoribbon, task versatility is demonstrated across nonlinear regression, spiral classification, and image recognition. The approach achieves near‐perfect accuracy on the Iris dataset and robust performance on MNIST digits, underscoring the potential of TA‐based encoding for physical computation. These results establish ultrafast TA spectroscopy as an experimentally accessible platform and lay the groundwork for future ultrafast, energy‐efficient photonic learning systems.

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R2 = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement.

Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum. Central to this capability are IR photodetectors (PDs) based on 2D materials (2DMs), which offer tunable spectral responsivity and wavelength‐resolved multiparameter optical information. This review examines the fundamental mechanisms and design strategies that enable spectral tunability at the frontier of 2DM‐based IR PDs, elucidating how they offer unique opportunities to tailor spectral responses across a broad wavelength range through symmetry‐breaking induced by geometric (geometrically tunable spectral engineering) and electric‐field (electrically tunable spectral engineering) effects. These approaches collectively enable simultaneous optimization of spectral tunability and sensitivity without compromising wavelength coverage, speed, power efficiency, or scalability, while also providing polarization sensitivity, multiband detection, and self‐powered operation for edge‐integrated AI platforms, including computational spectroscopy, artificial vision, computing, and communications. This review outlines the key processes and integration requirements for scalable manufacturing, which are essential for establishing spectrally tunable 2DM‐based IR PDs as core building blocks of intelligent optoelectronics. Ultimately, the development of spectrally tunable 2DM‐based IR PDs will transform intelligent optoelectronic platforms for or with AI

Predicting the effects of dissolved organic matter derived from microalgal biochar on copper: Insights from spectral response and sorption capacity

Physiological and UAV-based spectral responses of soybean genotypes under drought stress and their application in biomass prediction

Optoelectronic synapse based on 2D metal-organic framework Cu3(HHTP)2 for neuromorphic processing of both visual and auditory information.

Digital subtraction angiography (DSA) is the gold standard modality for diagnostics and guidance for interventional procedures. Spectral imaging has previously been explored for DSA, but severe noise amplification from material decomposition has impeded clinical adoption. We present a novel joint processing strategy that leverages both temporal and spectral information for material decomposition to address this issue. We develop a model-based material decomposition approach that utilizes the pre- and post-contrast images simultaneously for material estimation. Performance was evaluated on a small-vessel phantom on a test bench with a photon-counting detector. Joint processing was compared with temporal subtraction and previously proposed spectral DSA techniques including hybrid subtraction and conventional three-material decomposition. Additional simulation was performed to investigate performance with perfectly calibrated spectral response and sensitivity to patient motion. The improved conditioning of the proposed method effectively reduces bias and noise in the spectral results and allows three-material decomposition with dual-energy spectral measurements. The method achieved more than an order of magnitude variance reduction compared to previously proposed spectral DSA techniques. Compared to temporal subtraction, a mean variance reduction of 23.9% was achieved in simulation and 10.8% in experimental data. The degree of reduction is object-dependent. Noise reduction achieved in physical experiments is slightly lower than that in simulation, likely due to bias from imperfect spectral calibration. The method is equally sensitive to motion compared to temporal subtraction. The proposed method addresses a major image quality challenge limiting previous approaches and outperforms temporal subtraction. Such improvements facilitate the clinical translation of spectral angiography.

Polarization electric field tuning interface electric field in full-spectral response double S-scheme heterojunction to drive synchronous removal of Cr(VI) and RBR X-3B.

Abstract Thrombus composition and microstructure play a critical role in determining the treatment success for thrombus-related diseases such as ischemic stroke and deep vein thrombosis. However, no in vivo diagnostic method can fully capture thrombus microstructure yet, hindering personalized treatment. Photoacoustic imaging is uniquely positioned to provide information on thrombi composition as it relays optical absorption information from diffuse photons at acoustic propagation depths. Computational modeling enables systematic exploration of microstructural effects on imaging signals, offering insights into developing improved in vivo diagnostic techniques. However, no photoacoustic simulation platform can model microstructural features within centimeter-scale phantoms at reasonable computational cost. In this work, we present REFINE, a topology-driven framework for generating in silico thrombi replicating its key replicating their key microstructural traits. Unlike existing methods, REFINE enables controlled, recursive optimization of thrombus topology, making it suitable for accurate photoacoustic modeling and potentially powerful for biomechanical analyses beyond this study. These digital thrombi are embedded into a multiscale photoacoustic simulation platform that bridges microscale acoustic modeling with macroscale thrombus geometries, enabling efficient and realistic simulation of photoacoustic signal responses. We successfully created unique representation of thrombi microstructure for various compositions and porosities. Our simulation framework effectively links microstructural features to macroscale imaging outcomes, in agreement with previous empirical studies. Our simulation results demonstrate that thrombus microstructure significantly affects photoacoustic spectral responses and can be reliably modeled in silico. These findings highlight the potential of a multiscale photoacoustic simulation approach as a powerful tool for characterizing tissue microstructure and demonstrate the utility of our computational framework for in silico thrombi analysis and the development of diagnostic imaging strategies.

Mechanically exfoliated MoTe2 thin film Photodetector with an ultra-broadband spectral response from ultraviolet to short-wavelength infrared

A self-powered and cost-effective rGO/n-Si photodetector with broad spectral response including visible, UV, and near-IR regions

This study investigates the use of Si and CdTe-based Timepix3 detectors for photovoltaic energy conversion using solar X-rays and other high-energy electromagnetic radiation in space. As space missions increasingly rely on miniaturized platforms like CubeSats, power generation in compact and radiation-prone environments remains a critical challenge. Conventional solar panels are limited by size and spectral sensitivity, prompting the need for alternative energy harvesting solutions—particularly in the high-energy X-ray domain. A novel CubeSat-compatible payload design incorporates a UV-visible filter to isolate incoming X-rays, which are then absorbed by semiconductor detectors to generate electric current through ionization. Laboratory calibration was performed using Fe-55, Ba-133, and Am-241 sources to compare spectral response and clustering behaviour. CdTe consistently outperformed Si in detection efficiency, spectral resolution, and cluster density due to its higher atomic number and material density. Equalization techniques further improved pixel threshold uniformity, enhancing spectroscopic reliability. In addition to experimental validation, simulations were conducted to quantify the expected energy conversion performance under orbital conditions. Under quiet-Sun conditions at 500 km LEO, CdTe absorbed up to 1.59 µW/cm2 compared to 0.69 µW/cm2 for Si, with spectral power density peaking between 10 and 20 keV. The photon absorption efficiency curves confirmed CdTe’s superior stopping power across the 1–100 keV range. Under solar flare conditions, absorbed power increased dramatically, up to 159 µW/cm2 for X-class and 15.9 µW/cm2 for C-class flares with CdTe sensors. A time-based energy model showed that a 10 min X-class flare could yield nearly 1 mJ/cm2 of harvested energy. These results validate the concept of a compact photovoltaic payload capable of converting high-energy solar radiation into electrical power, with dual-use potential for both energy harvesting and radiation monitoring aboard small satellite platforms.

Abstract This work proposes a synergistic thickness engineering and interface optimization strategy to achieve the controllable fabrication of high‐performance PtSe 2 /PdSe 2 van der Waals heterostructure photodetectors. Precise control over the PtSe 2 layer thickness, which is achieved at target values of 30 and 40 nm using reactive ion etching, induces a transition from ambipolar to strong n‐type behavior, shortening the carrier transit path. Combined with the in situ preparation of PdSe 2 using laser direct‐write lithography and thermal‐assisted conversion, an atomically matched heterointerface is formed, significantly reducing interfacial defect density. Broadband spectral response spanning 532 to 2200 nm, maintaining stable photocurrent response at 1850 and 2200 nm, a peak responsivity of 1.232 A W −1 (at 532 nm under a bias of −2 V), and a specific detectivity up to 9.82 × 10 9 Jones (1310 nm), a 3‐dB cutoff frequency of 16.6 kHz with fast rise/fall times of 6.13/32.88 µs, enabling polarization‐resolved infrared imaging. Furthermore, a polarization‐modulated optical communication system is implemented, demonstrating the device's application potential. This work presents a scalable platform for high‐speed, polarization‐resolved photodetectors, establishing a new paradigm of thickness‐interface engineering in 2D heterostructures for advanced infrared imaging and intelligent sensing.

Nonlinear optical metasurfaces enable subwavelength control of light-matter interactions, yet simultaneous tunability of harmonic signal intensity and spectral response remains a fundamental challenge. Here, a local-to-nonlocal second harmonic (SH) generation process is presented, enabled by an electrically tunable polaritonic metasurface, allowing independent control of the SH spectral peak wavelength and intensity. The metasurface combines a localized surface plasmon resonance at the fundamental frequency with a transverse magnetic guided-mode resonance at the SH frequency. By engineering modal overlap within a multiple quantum well layer, voltage-controlled modulation of SH intensity and angle-controlled spectral tuning is achieved, demonstrating two decoupled degrees of freedom associated with local and nonlocal modes. Angle-resolved nonlinear reflection measurements confirm the independent tunability of the metasurface, validating the separation of excitation and emission pathways. This hybrid approach provides a general framework for nonlinear metasurfaces with enhanced flexibility and functional control, paving the way for applications in nonlinear signal processing, angle-multiplexed photonics, and entangled photon-pair generation for quantum optics.

Moisture content is one of the key indicators for evaluating the quality of apricots. When moisture levels fluctuate over an excessively wide range, scattering effects and absorption characteristics interfere with each other, making it difficult for a single model to achieve accurate predictions across the entire range. This study investigates precision modeling methods applicable to different moisture intervals based on spectral morphological features. By extracting the spectral morphological features of the water-sensitive regions (peak and valley) and conducting Pearson correlation analysis, the spectral morphological feature parameters with relatively strong correlations were selected, and they were combined with the characteristic bands to construct a segmented model for water content intervals. The results indicate that spectral morphological features of apricots within the 25–40% and 40–55% moisture range exhibit a certain correlation with moisture content. A weak correlation is observed in the 55–70% moisture range. After preliminary fusion modeling of spectral morphological features and characteristic bands for apricots across different moisture ranges, further analysis revealed that moisture content models based on valley morphology features and characteristic bands outperformed those based on peak morphology features and characteristic bands, demonstrating superior representational capability. By establishing a fusion model based on the spectral morphological parameters selected through Pearson’s method and the characteristic bands, the detection accuracy and model stability in the 25–70% moisture content range have been effectively improved. Among all the models covering different moisture content ranges, the model for the 55–70% moisture content range has the best prediction effect. The correlation coefficient of its prediction set reaches 0.8723, and the Ratio of Performance to Interquartile Range (RPIQ) is 2.5220, indicating that this range is the most suitable for establishing a high-precision quantitative moisture content detection model. This research effectively solved the problem of spectral response distortion caused by wide variations in moisture content and improved the prediction accuracy of the moisture content detection model for apricots.

Two-dimensional (2D) MXenes have recently emerged as a distinctive class of materials for advanced thermal management due to their combination of high electrical conductivity, broadband optical absorption, low or high infrared (IR) emissivity, efficient light-to-heat conversion, and anisotropic thermal conductivity. This review provides a thorough overview of MXene interaction with IR radiation and MXene-based thermal management strategies, emphasizing the fundamental mechanisms governing spectral response, heat generation, transfer, and radiative emission. We review recent progress in the development of MXene films, coatings, aerogels, fibers, and composites for applications such as ultrathin thermal insulation, IR camouflage, photothermal therapy, wound healing, solar water desalination, wearable heaters, deicing systems, soft actuators, and separation membranes. Finally, we address prevailing challenges and future research directions, offering perspectives to facilitate the advancement of next-generation MXene-based materials for advanced thermal management.