Visible To Near-infrared Research Articles

Low‐Coordination Crystallographic Lattice Engineering to Discover Ce3+‐Activated Ultra‐Wide Visible‐to‐Near‐Infrared Luminescent Materials

AbstractDevelopment of ultra‐broadband visible‐to‐near‐infrared (VIS‐NIR) luminescent materials is a challenging but rewarding endeavor, as they are highly desirable for a number of applications in photonics, optoelectronics, and biological sciences. Nevertheless, to date, rare earth‐doped phosphors that can be efficiently excited by blue light and produce broadband emission in the VIS‐NIR regions remain scarce. In this study, a series of Ce3+‐doped Ba3M4O9 (M = Sc, Lu, Y) broadband luminescent materials are designed based on low‐coordination crystallographic lattice engineering. Among these materials, Ba3Y4O9:Ce3+ produces a continuous ultra‐wide VIS‐NIR emission (λem = 660 nm) in the range of 500–950 nm under 410 nm light excitation. This emission wavelength is the longest among those observed in Ce3⁺‐doped oxide materials. Structural refinement, theoretical calculation, and spectral analysis demonstrate that this VIS‐NIR broadband emission is attributed to the occupying multiple distorted octahedral lattice sites by Ce3+ in Ba3Y4O9. Several strategies are employed to enhance the luminescence intensity and thermal stability of this material, including the use of carbon paper coating, the incorporation of co‐solvents, and the addition of GeO₂ or ZrO₂. Furthermore, the as‐prepared materials demonstrate multifunctional applications in white light‐emitting diodes (WLED), night vision, and fluorescent thermometers. This study offers insights into the design of Ce3+‐doped ultra‐wide VIS‐NIR light‐emitting materials.

Multifunctional integrated droplet lens based on microfluidics

A multifunctional integrated droplet lens based on microfluidics is proposed. The lens consists of a microfluidic chip, four droplets with different properties, and transparent silicone oil. Visible-light, infrared, narrow wavelength band, and light-switching droplets can be used for imaging in the VIS-NIR (visible to near-infrared), narrow wavelength band, and blocking light, respectively. The optical focal power of the proposed lens is -134.6 D ∼ -91.62 D with visible-light droplet and -21.1 D ∼ -12.9 D with infrared droplet. When the droplet lens is used as an optical switch, the maximum optical attenuation is 251:1. It is measured that the transmitted power can be adjusted from 0.015 mW to 0.096 mW when the initial incident power is 0.776 mW. The proposed lens integrates the functions of zoom and optical attenuation. It offers important advantages in providing lightweight, high integration and shows a wide range of potential applications in microscopy systems, microfluidic systems, and variable optical attenuators.

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

High‐Gain and Tunable Linear Photodetection in 2D Tunneling Heterostructures Through Potential Engineering

Abstract2D materials are extensively employed in the fabrication of high‐performance photodetectors owing to their exceptional physical properties. However, most 2D material photodetectors fail to sustain high gain under intense illumination due to the limited intrinsic trap states. Here, an n‐n type Bi2O2Se/SnSe2 van der Waals tunneling heterojunction photodetector with a detection range from visible to near‐infrared (VIS‐NIR) is presented. Under reverse bias, the heterojunction induces a significant electron barrier and hole potential well, ensuring low leakage current and ample hole defect states. Therefore, the photodetector demonstrated a responsivity of 1636.3 AW−1 and a detectivity of 1.39 × 1014 Jones under 660 nm illumination, maintaining a tunable linear dynamic range (LDR) of ≈74.7 dB. This performance is attributed to the hole potential well‐suppressing the recombination of photogenerated carriers, thereby enhancing the device's gain. Furthermore, the tunneling of photogenerated electrons within the heterojunction's space charge region under bias enables rapid response (75.1 and 15.6 µs). In summary, the study introduces a novel strategy to overcome the limited detection capabilities of 2D devices under intense illumination, characterized by outstanding linearity for rapid detection and high‐resolution imaging.

Novel multi-function setup comprising ultra-bright uniform source for spectral and radiometric calibrations of digital imaging sensors and multiple spectrometric applications

Abstract A new setup is being developed for calibrating CCD/CMOS sensors and cameras, and measuring spectrophotometric quantities in both point-wise and imaging modes. The setup includes a laser-driven light source (LDLS), double-grating monochromator, and an integrating sphere with specific design for two-dimensional (2D) spectrophotometric measurements of complex samples and spectral responsivity calibration of digital imaging sensors and cameras in the visible to near infrared (VIS-NIR) region. The setup can cover a spectral range from 250 nm to 2000 nm with relatively high radiance. The study presents optimization and validation work in the visible through NIR spectral range, up to 1000 nm. Preliminary experiments suggest improved accuracy and lower uncertainties for better metrological performance and sophisticated applications. The developed instrument promises a variety of novel applications while suppressing systematic errors and inaccuracies. The presented measurements results include the validation of 2D spectral reflectance/transmittance measurements in the 360-1000 nm spectral region using selected homogenous standards. Additionally provided are the findings of demonstration measurements for 2D spectral transmittance and the CCD camera’s spectral responsivity within the same spectral range.

UAV hyperspectral image acquisition and processing, an application for nutrient estimation of rice in Vietnam

Hyperspectral imagery obtained from Unmanned Aerial Vehicles (UAVs) is increasingly employed to investigate nutrient concentrations in vegetation. The deployment of a hyperspectral camera on a UAV, flight planning, image acquisition, preprocessing of hyperspectral data, and the subsequent estimation of nutrient concentrations in vegetation are facing challenges. These challenges manifest as geometric, spectral distortions, and the abundance of numerous spectral bands. This study seeks to guide on mitigating the impact of issues encountered during an experiment to estimate nutrient concentrations in rice leaves using UAV hyperspectral images. An industrial hexagonal drone equipped with a push-broom hyperspectral camera featuring 122 bands within the Visible to Near-Infrared (VIS-NIR) wavelength range (400-960 nm) is employed to collect data over a 1-hectare testing rice field. Models for estimating Leaf Phosphorus Concentration (LPC) and Leaf Potassium Concentration (LKC) are developed based on the correlation between hyperspectral images, characterized by a 3 cm spatial resolution, and 162 LPC and 162 LKC reference data points. Utilizing various vegetation indices for LPC and LKC estimation, the outcomes reveal that a combination of band wavelengths at 838 nm and 734 nm is effective for LPC estimation, yielding a Root Mean Square Error (RMSE) of 27.1%. Conversely, LKC estimation exhibits an RMSE of 38.8% with an insignificant correlation between LKC and the current wavelength ranges. Above all, this study is a primary example of the utilization of UAV hyperspectral data in precision agriculture in Vietnam.

Numerical analysis of a single channel exposed core elliptical shaped PCF based highly sensitive SPR sensor for wide RI sensing.

This study presents a numerical study of a highly sensitive photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor capable of detecting five types of cancer and bacterial contamination in water. By precisely arranging only two air holes in a single channel of an elliptical-shaped PCF, the sensor maximizes interaction between core-guided modes and surface plasmon polaritons (SPP) along the fiber. Evaluation using COMSOL Multiphysics simulation software, based on finite element method (FEM), demonstrates outstanding sensor performance across a wide refractive index (RI) range (1.33 to 1.43). With a maximum wavelength sensitivity (WS) of 188,000 nm/RIU and amplitude sensitivity (AS) of -22,377.99 RIU-1, the sensor achieveStructural Design and Methodologys a sensor resolution (SR) of 5.3191 × 10-7 RIU and figure of merit (FOM) of 854.55 RIU-1. Notably, it exhibits AS and WS values tailored for specific cancer cell types and water contamination. These results endorse the sensor's potential in diverse biological and molecular analyte RI detection applications within the visible to near-infrared (VNIR) range (0.55 to 4 µm), offering high sensitivity, affordability, wide sensing range, good linearity, low propagation loss, and simplicity in construction.

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible‐Near‐Infrared to Thermal Infrared

AbstractSuper‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition.

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation. This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like. A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data. The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process. The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

Design and Analysis of Multi-Analyte Detection Based Biosensor in the Visible to Near-Infrared (VNIR) Region.

This manuscript introduces a highly sensitive dual-core photonic crystal fiber (PCF) based multi-analyte surface plasmon resonance (SPR) sensor, possessing the ability to detect multiple analytes at once. A chemically stable thin plasmonic substance of gold (Au) layer, holding a thickness of 30 nm, is employed to the outer portion of the stated design that manifests a negative real permittivity. Moreover, an ultra-thin film of aluminum oxide (Al2O3) , having a thickness of 10 nm, is inserted into the exterior of the gold film to calibrate the resonance wavelength as well as magnify the coupling strength. The performance of the sensor is rigorously explored employing the finite element method (FEM), where numerical investigation confirms that the intended sensor model exhibits a peak amplitude sensitivity (AS) of 2606 RIU-1 , as well as a highest wavelength sensitivity (WS) of 20,000 nm/RIU. The achieved outcomes affirm that the sensor design can be conceivably applied in numerous biological; as well as biochemical analyte refractive index (RI) detection to realize the relevant significant applications in the visible to near-infrared (VNIR) region of 0.5 to [Formula: see text].

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging.

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries' Pomological Traits.

Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400-1000 nm) and short-wave infrared (SWIR) (935-1720 nm) for predicting four strawberry quality attributes (firmness-FF, total soluble solid content-TSS, titratable acidity-TA, and dry matter-DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product's marketability.

HYPERSPECTRAL UAS IMAGERY FOR GRASS SWARDS BIOMASS AND NITROGEN ESTIMATION

Abstract. Monitoring agricultural grass fields is particularly important for meat and milk production in Northern Europe, where three harvests occur during a growing season to maximize yields. Reliable data on forage, including biomass and nitrogen concentration, are essential for making informed decisions regarding seed mixtures, fertilizer rates, and harvest timing. Miniaturized hyperspectral cameras mounted on unmanned aerial systems (UAS) have become increasingly accessible and efficient. These cameras, operating in the visible to near-infrared (VNIR) range, have shown potential in estimating grass sward quantity and feeding quality. Additional advancements in hyperspectral technology have emerged the short-wave infrared (SWIR) range for UAS applications, previously utilized mainly in laboratory and aircraft-based systems. This study aims to explore the potential of VNIR and SWIR hyperspectral UAS-based remote sensing in biomass and nitrogen estimation during primary and re-growth stages. Grass fresh yield and nitrogen concentration prediction models were built after selecting the most significant features from the cameras to cope with the high dimensionality of the data. Using best features and machine learning, both fresh yield and nitrogen concentration were estimated with normalized root mean square error better than 10%. This work contributes to the development of accurate remote sensing techniques, supporting sustainable agricultural practices and climate change studies.

Estimation of Total Nitrogen Content in Topsoil Based on Machine and Deep Learning Using Hyperspectral Imaging

Excessive total nitrogen (TN) content in topsoil is a major cause of eutrophication when nitrogen flows into water systems from soil losses. Therefore, TN content prediction is essential for establishing topsoil management systems and protecting aquatic ecosystems. Recently, hyperspectral imaging (HSI) has been used as a rapid, nondestructive technique for quantifying various soil properties. This study developed a machine and deep learning-based model using hyperspectral imaging to rapidly measure TN contents. A total of 139 topsoil samples were collected from the four major rivers in the Republic of Korea. Visible-to-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging data were acquired in the 400–1000 nm and 895–1720 nm ranges, respectively. Prediction models for predicting the TN content in the topsoil were developed using partial least square regression (PLSR) and one-dimensional convolutional neural networks (1D-CNNs). From the total number of pixels in each topsoil sample, 12.5, 25, and 50% of the pixels were randomly selected, and the data were augmented 10 times to improve the performance of the 1D-CNN model. The performances of the models were evaluated by estimating the coefficients of determination (R2) and root mean squared errors (RMSE). The Rp2 values of the optimal PLSR (with maximum normalization preprocessing) and 1D-CNN (with SNV preprocessing) models were 0.72 and 0.92, respectively. Therefore, HSI can be used to estimate TN content in topsoil and build a topsoil database to develop conservation strategies.

Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense

This study proposes a method to extract the signature bands from the deep learning models of multispectral data converted from the hyperspectral data. The signature bands with two deep-learning models were further used to predict the sugar content of the Syzygium samarangense. Firstly, the hyperspectral data with the bandwidths lower than 2.5 nm were converted to the spectral data with multiple bandwidths higher than 2.5 nm to simulate the multispectral data. The convolution neural network (CNN) and the feedforward neural network (FNN) used these spectral data to predict the sugar content of the Syzygium samarangense and obtained the lowest mean absolute error (MAE) of 0.400° Brix and 0.408° Brix, respectively. Secondly, the absolute mean of the integrated gradient method was used to extract multiple signature bands from the CNN and FNN models for sugariness prediction. A total of thirty sets of six signature bands were selected from the CNN and FNN models, which were trained by using the spectral data with five bandwidths in the visible (VIS), visible to near-infrared (VISNIR), and visible to short-waved infrared (VISWIR) wavelengths ranging from 400 to 700 nm, 400 to 1000 nm, and 400 to 1700 nm. Lastly, these signature-band data were used to train the CNN and FNN models for sugar content prediction. The FNN model using VISWIR signature bands with a bandwidth of ± 12.5 nm had a minimum MAE of 0.390°Brix compared to the others. The CNN model using VISWIR signature bands with a bandwidth of ± 10 nm had the lowest MAE of 0.549° Brix compared to the other CNN models. The MAEs of the models with only six spectral bands were even better than those with tens or hundreds of spectral bands. These results reveal that six signature bands have the potential to be used in a small and compact multispectral device to predict the sugar content of the Syzygium samarangense.

Al Huwaysah 010: The most reduced brachinite, so far

AbstractAl Huwaysah 010 is an ungrouped achondrite meteorite, recently referred to as a brachinite‐like meteorite. This meteorite, showing a fine‐grained assemblage of low‐Ca pyroxene and opaque phases, is strongly reduced in comparison to other reduced brachinites. The occurrence of some tiny plates of graphite and oldhamite in this meteorite suggests that a partial melt residue has experienced a further reduction process. Olivine, the most abundant phase, is compositionally homogeneous (Fo83.3) as well as the clinopyroxene (En45.5Fs10.8Wo43.7) and the plagioclase (Ab69.5). Orthopyroxene (En85.4Fs13.9Wo0.7) also occurs but only in a fine intergrowth. Other accessory phases are Fe metal grains (Ni‐free or Cr‐bearing Fe‐Ni alloy), troilite, chlorapatite, pentlandite (as inclusions in chromite). The sample shows two different closure temperatures: the highest (≈900°C) is determined via the olivine–chromite intercrystalline geothermometer and the lowest temperature (≈520°C) is determined via the pyroxene‐based intracrystalline geothermometer. These temperatures may represent, respectively, the closure temperature associated with the formation and a subsequent impact event excavating the sample from the parental body. The visible to near‐infrared (VNIR) reflectance spectra of Al Huwaysah 010 exhibit low reflectance, consistent with the presence of darkening components, and weak absorptions indicative of olivine and pyroxene. Comparing the spectral parameters of Al Huwaysah 010 to potential parent bodies characterized by olivine–pyroxene mineralogy, we find that it falls within the field previously attributed to the SIII type asteroids. These results lead us to classify the Al Huwaysah 010 meteorite as the most reduced brachinite, whose VNIR spectral features show strong affinities with those of SIII asteroids.

Evaluating the effects of space weathering on magnetite on airless planetary bodies

Magnetite is a relevant mineral component of asteroids as it has been identified in carbonaceous chondrites, on the surface of asteroid Bennu through remote sensing observations, and in samples returned from asteroid Ryugu. However, the effects of space weathering processes on magnetite have not yet been explored. To investigate how this mineral phase responds to space weathering, here we simulate micrometeoroid bombardment and solar wind irradiation of magnetite using pulsed laser and ion irradiation experiments. We performed X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and visible to near-infrared (VNIR) reflectance spectroscopy analyses to characterize the chemical, microstructural, and spectral response of magnetite to simulated space weathering. In addition, we carried out ion impact simulations using the SDTrimSP software to evaluate the calculated response of magnetite to 1 keV H+ and 4 keV He+ ions and compared these results to our XPS and TEM results. Ion irradiation simulated ∼750 years on the surface of asteroid Bennu, with a solar-wind appropriate total H:He fluence ratio (∼24). Within this time, depletion of O was observed with H+ and He+ ion irradiation, with significantly greater change via protons due to the larger fluence, where preferential sputtering promotes the formation of a metallic iron layer at the magnetite surface. This suggests that solar wind ions act as reducing agents on Fe oxides, with a fraction remaining implanted in these phases. Indeed, we observe elongated defects contained in a crystalline rim created by He+ implanted ions in the TEM. Pulsed laser irradiation, analogous to micrometeoroid impacts, generates melts on the surface of the magnetite grains. The impact melts and H+-generated metallic iron rims both result in increased VNIR spectral reflectance, but lower fluence He+ implantation has no significant spectral effect. These results suggest that space weathered magnetite could contribute to bright regions detected in remote sensing analyses of the Ryugu and Bennu surfaces by the Hayabusa2 and OSIRIS-REx missions and will contribute to the identification and interpretation of space weathered magnetite in returned samples retrieved from both asteroids.

Visible to near-infrared single pixel microspectrometer using electrothermal MEMS grating.

Compact spectrometers facilitate non-destructive and point-of-care spectral analysis. Here we report a single-pixel microspectrometer (SPM) for visible to near-infrared (VIS-NIR) spectroscopy using MEMS diffraction grating. The SPM consists of slits, electrothermally rotating diffraction grating, spherical mirror, and photodiode. The spherical mirror collimates an incident beam and focuses the beam on the exit slit. The photodiode detects spectral signals dispersed by electrothermally rotating diffraction grating. The SPM was fully packaged within 1.7 cm3 and provides a spectral response range of 405 nm to 810 nm with an average 2.2 nm spectral resolution. This optical module provides an opportunity for diverse mobile spectroscopic applications such as healthcare monitoring, product screening, or non-destructive inspection.

Sub-1 Volt and high-bandwidth visible to near-infrared electro-optic modulators

Integrated electro-optic (EO) modulators are fundamental photonics components with utility in domains ranging from digital communications to quantum information processing. At telecommunication wavelengths, thin-film lithium niobate modulators exhibit state-of-the-art performance in voltage-length product (VπL), optical loss, and EO bandwidth. However, applications in optical imaging, optogenetics, and quantum science generally require devices operating in the visible-to-near-infrared (VNIR) wavelength range. Here, we realize VNIR amplitude and phase modulators featuring VπL’s of sub-1 V ⋅ cm, low optical loss, and high bandwidth EO response. Our Mach-Zehnder modulators exhibit a VπL as low as 0.55 V ⋅ cm at 738 nm, on-chip optical loss of ~0.7 dB/cm, and EO bandwidths in excess of 35 GHz. Furthermore, we highlight the opportunities these high-performance modulators offer by demonstrating integrated EO frequency combs operating at VNIR wavelengths, with over 50 lines and tunable spacing, and frequency shifting of pulsed light beyond its intrinsic bandwidth (up to 7x Fourier limit) by an EO shearing method.