Spectral Devices Research Articles

Spectral Discrimination of Common Karoo Shrub and Grass Species Using Spectroscopic Data

Rangelands represent about 25% of the Earth’s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (>70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ analytical spectral device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands.

Correlation between glycemic control and retinal nerve fiber layer parameters in type 2 diabetics without retinopathy: a case control study

Background Retinal neurodegenerative changes were found to develop in patients with diabetes mellitus (DM) before the onset of microvascular changes of diabetic retinopathy (DR). Such changes usually affect inner retinal layers in the form of retinal nerve fiber layer (RNFL) thinning. Aim To correlate the degree of glycemic control [in the form of hemoglobin A1c (HBA1c) levels] with changes in peripapillary RNFL thickness in patients with type 2 DM without DR using optical coherence tomography (OCT). Patients and methods Participants are divided into three groups each including 28 eyes: group 1: uncontrolled type 2 DM without DR (HBA1c≥7%), group 2: controlled type 2 DM without DR (HBA1c<7%), group 3: healthy age and sex-matched controls. Peripapillary RNFL and macular ganglion cell complex (GCC) thickness were evaluated in all participants using RTVue spectral domain OCT device (Optovue, Fremont, USA). Results Peripapillary RNFL and macular GCC were significantly thinner in the uncontrolled type 2 DM group in comparison to both the controlled type 2 DM group and the healthy control group. No significant difference in OCT parameters between the controlled type 2 DM group and the healthy control group. HBA1c shows a statistically significant negative correlation with average, superior and inferior RNFL thickness as well as average, superior, and inferior GCC thickness. Conclusion Early and adequate glycemic control, indicated by HBA1c less than 7%, is of critical importance in delaying and reducing early neurodegenerative changes in the form of RNFL and GCC thinning in patients with type 2 DM without DR.

High-Precision Chromatic Confocal Technologies: A Review

Chromatic confocal technology is widely used for precise, steady, and efficient displacement measurement in many industrial fields. It employs the confocal and dispersion principles to encode axial positions with the wavelengths of the reflected broad spectrum. The typical chromatic confocal sensor includes a light source, a dispersion objective, conjugate pinholes, and a spectral detection device. This study offers an overview of the current research on chromatic confocal technology. Because of its good performance in displacement detection, chromatic confocal technology has been widely used in contour measurement, biomedical imaging, and thickness measurements, as part of global and professional research. Due to its structural flexibility, it is also easily integrated into industrial equipment for in-machine and online profile measurements. It holds significant potential for future applications in industrial manufacturing and scientific research. However, there are also some challenges to be explored in terms of the broadband light source, dispersive optics design, and the balance between speed and accuracy in signal processing.

High-Performance Thermally Evaporated Blue Perovskite Light-Emitting Diodes Enabled by Post-Evaporation Passivation

Thermally evaporated perovskite light-emitting diodes (PeLEDs) are promising for next-generation displays, yet process-compatible passivation strategies for performance enhancement are still lacking. Herein, an effective in-situ surface passivation strategy using post-evaporated metformin hydrochloride (MFCl) is introduced in co-evaporated PeLEDs. MFCl post-deposited onto the perovskite film induces surface reconstruction, improving surface uniformity and healing grain boundaries. Additionally, MFCl penetrates deeply into the perovskite film, passivating both undercoordinated Pb and halide ions at grain boundaries. By employing this strategy, we achieve thermally evaporated blue PeLEDs with a peak external quantum efficiency (EQE) of 9.2 % and a maximum luminance of 1820 cd/m−2(−|-), emitting at a peak wavelength of 488 nm. Furthermore, this strategy effectively suppresses ion migration, significantly enhancing the spectral stability and device lifetime of the blue PeLEDs. This work provides a process-compatible defect passivation strategy for thermally evaporated PeLEDs, laying the foundation for further advancements in this field.

Prediction of secondary metabolites in maize under different nitrogen inputs by hyperspectral sensing and machine learning

Flavonoids are compounds resulting from secondary plant metabolism that provide benefits to human health by food. This study aimed to accuracy of predicting flavonoids in maize plants subjected to different nitrogen rates using hyperspectral reflectance and machine learning (ML) algorithms. The experiment was carried out in randomized blocks in a 4 × 5 factorial design (four N inputs: 0; 30; 60 and 120 % of the recommended N input; and five readings of the reflectance spectra in maize leaves from different vegetative stages: V6, V8, V10, V12 and V14, in four replications, totaling 80 treatments. N rates were applied in the V4 and V8 phenological stages, using urea as the N source. For hyperspectral analysis, four leaves from each treatment were collected and analyzed using a spectroradiometer (FieldSpec 4 HRes, Analytical Spectral Devices), capturing the spectrum in the 350 to 2500 nm range. Subsequently, the leaf samples used in the reflectance readings were dried, ground and subjected to isoflavone quantification, analyzed by ultra-performance liquid chromatography in three repetitions, quantifying daidzein 1 (D1), daidzein 2 (D2), genistein 1 (G1), genistein 2 (G2), and total isoflavones. Data obtained was subjected to machine learning analysis, testing two data set input configurations: wavelengths (WL) and calculated spectral bands (B), and D1, D2, G1, G2 and total isoflavones as output variables. The ML algorithms tested were artificial neural networks (ANN), REPTree (DT), M5P decision tree (M5P), ZeroR (R), Random Forest (RF) and support vector machine (SVM), evaluated according to their performance by the correlation coefficient (r) and mean absolute error (MAE). The results show that the SVM algorithm had the highest accuracy in predicting the variables D1, D2, G1, G2 and total isoflavones, outperforming the other algorithms when WL was used as input in dataset.

Measurement of particle size in suspension based on VIS-NIR-RGB sensors and gradient-boosted regression tree

To better utilize the spectral features of near-infrared and visible light and address issues in traditional suspension particle size detection, such as long measurement cycles, high costs, and complex operation, we propose a new method that combines multispectral sensor characterization technology with machine learning algorithms for rapid particle size measurement. By integrating a lab-designed light source driving circuit and spectral data acquisition device, we obtained 15 spectral data points for visible light, near-infrared, and red–blue–green (RGB) values to build a prediction model. The near-infrared-based model obtained a coefficient of determination (R2) of 0.9130, whereas models combining RGB-near-infrared and visible-near-infrared features performed best, with R2 values of 0.9670 and 0.9845. These models improved performance by 5.91% and 7.83%, effectively overcoming the limitations of single spectral features in different suspensions. This combination of visible and near-infrared light with machine learning proves effective for detecting average particle size in suspensions.

Nanophotonic Bragg grating assisted Mach–Zehnder interferometers for O-band add-drop filters

Spectral filters are fundamental building blocks in integrated photonics. Bragg grating filters have been demonstrated in silicon waveguides with a wide range of spectral responses and are suitable for wavelength division multiplexing applications. However, retrieving Bragg grating reflections typically requires external components such as fiber optic circulators. In this work, we develop fully integrated add-drop filters based on cladding-modulated Bragg gratings incorporated in a Mach–Zehnder interferometer configuration. We design complex spectral filtering devices with single and dual-band flat-top responses for the specified bandwidth. Additionally, we propose a novel design methodology which aims to minimize phase errors within the filters. We experimentally demonstrate add-drop filters with single-band and two-band rejection spectra at the datacom O-band, fabricated on a 220-nm thick silicon-on-insulator platform. Our results show an insertion loss below 1 dB and a crosstalk of around −20 dB at the channel center for a 4.5-nm wavelength grid and 3-nm wide channels.

Can retinal nerve fiber layer (RNFL) thickness be a marker for distinguishing bipolar depression from unipolar depression?

Objective We aimed to compare retinal nerve fiber layer (RNFL) thickness and ganglion cell complex (GCC) thickness in bipolar disorder (BD) and major depressive disorder (MDD). Method The study included thirty MDD, thirty-two BD participants in depressive episode, and thirty-seven controls matched according to age, gender, body mass index (BMI), and smoking status. Optic coherence tomography (OCT) measurements were performed for both participants and controls. The RNFL and GCC thickness were measured and recorded automatically by a spectral OCT device. Participants were also subjected to Hamilton Depression Rating Scale (HAM-D). Results RNFL superior thickness was significantly lower in BD participants, compared to the MDD participants and controls (p = 0.001). GCC inferior (p = 0.022) and inferonasal (p = 0.005) thickness were detected lower in BD group, compared to the control groups. In the BD group, HAM-D scores were negatively correlated with RNFL-temporal (p = 0.049, r= −0.357), GCC inferotemporal (p = 0.02, r= −0.416) and superotemporal thickness (p = 0.002, r= −0.546). Conclusions RNFL thickness were lower in BD participants compared to the MDD and controls and, GCC thickness were lower in BD participants compared to the controls. Our findings support the hypothesis that neurodegeneration is part of the pathogenesis of BD. Future research are needed to confirm the lack of RNFL thickness in MDD, which could have immediate therapeutic consequences as well as implications for distinguishing BD from MDD.

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

PurposeCover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.MethodsThe objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.ResultsModels using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.ConclusionAs spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R2 value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration.

The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data

Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms—it helps the soil’s biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and time-efficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 °C), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area’s average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400–2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale.

A research on the effect of plasma spectrum collection device on LIBS spectral intensity

Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information. To overcome this limitation, this study presents a novel method for acquiring plasma spectral information from various spatial directions. A parabolic-shaped plasma spectral collection device (PSCD) is employed to effectively collect more spectral information into the spectrometer, thereby enhancing the overall spectral intensity. The research objects in this study were soil samples containing different concentrations of heavy metals Pb, Cr, and Cd. The results indicate that the PSCD significantly enhances the spectral signal, with an enhancement rate of up to 45%. Moreover, the signal-to-noise ratio also increases by as much as 36%. Simultaneously, when compared to the absence of a device, it is found that there is no significant variation in plasma temperature when the PSCD is utilized. This observation eliminates the impact of the spatial effect caused by the PSCD on the spectral intensity. Consequently, a concentration-spectral intensity relationship curve is established under the PSCD. The results revealed that the linear fitting R 2 for Pb, Cr, and Cd increased by 0.011, 0.001, and 0.054, respectively. Additionally, the limit of detection (LOD) decreased by 0.361 ppm, 0.901 ppm, and 0.602 ppm, respectively. These findings indicate that the spectral enhancement rate elevates with the increase in heavy metal concentration. Hence, the PSCD can effectively enhance the spectral intensity and reduce the detection limit of heavy metals in soil.

Consideration of the influence of aberrations in the operation diffraction prism spectral device

Spectral analysis is one of the most important methods of physico-technical research for solving many scientific and practical problems. The key parameter of a spectral instrument is its resolving power. Instruments based on diffraction gratings face a fundamental limitation: their resolving power is limited to operation in the first two orders of diffraction. The limitation is due to the fact that the intensity of diffraction maxima drops sharply with increasing diffraction order. As a result, such devices are unable to distinguish closely spaced spectral lines. Increasing the resolving power of spectral priors is an important and urgent task for spectroscopy. The aim of the research was to develop an optical scheme of a spectral instrument based on a high-resolution prism for the detection of nearby spectral lines with minimal influence of aberrations. The optical scheme of the diffraction prism spectral instrument was developed. The possibility of detecting nearby spectral lines is shown. Also a comprehensive study of the influence of aberrations and the development of methods of their minimisation to achieve the optimum quality of the spectral instrument and accuracy of measurements is carried out. The obtained results confirm the possibility of increasing the resolving power of the spectral instrument for the detection of closely spaced spectral lines. Taking into account the influence of aberrations in the diffraction prism spectral instrument will improve the quality of its work and measurement accuracy. The obtained results open new possibilities for studying complex spectra with high detail.

A multispectral imaging system developed based on the spectral feature selection method for identification of housefly pupae

Hyperspectral imaging systems can simultaneously obtain spectral and spatial information of fly pupae, especially achieving a large area of spectral information collection in a short period. However, hyperspectral imaging systems are expensive and require much computation. This study attempts to develop a four-channel spectral imaging system composed of a camera and four filters to achieve spectral discrimination of housefly pupae over a large area. It also aims to verify the feasibility of using spectral feature selection methods to guide the development of low-cost specialized spectral systems. Meanwhile, a strategy combining spectral degradation with the traversal method was proposed for determining the parameter indicators of optical components. Firstly, use a microspectrometer to acquire the spectra of fly pupae. Secondly, the successive projections algorithm (SPA) extracts four feature wavelengths related to fly pupa recognition from the full spectrum variables. Then, based on the strategy of spectral degradation combined with the traversal method, the parameters of the four filters are determined. Finally, a 4-channel spectral imaging system was constructed, and a prediction model was built with random forest (RF), while the spectral index (SI) method was used to improve model performance. The results indicate that this low-cost multispectral imaging system can achieve high-performance discrimination of housefly pupae (accuracy = 97.44 %). At the same time, SPA, spectral degradation, and traversal methods can provide effective central wavelength and resolution indicators for constructing low-cost multispectral imaging systems, offering a practical and feasible example to guide the development of specialized low-cost spectral devices using spectral feature selection methods.

Solution Cathode Glow Discharge – Atomic Emission Spectrometry (SCGD-AES) With an Interference Filter for Spectral Discrimination for the Determination of Thallium in Surface Water

Solution cathode glow discharge atomic emission spectrometry (SCGD-AES) using wide-bandpass (10-nm) interference filters (IF) as a spectral discrimination device was used for the determination of thallium (Tl) in surface water. The use of wide-bandpass interference filters greatly reduces the cost of traditional detection instruments. The influence of solution flow rate, discharge current and pH on the detection limit was characterized. Moreover, matrix effect experiments were performed to study the effect of different concentrations of Na+, K+, Ca2+, and Mg2+ on the Tl signal intensity. Using a solution flow rate of 2.8 mL/min, a discharge current of 50 mA, and pH of 1.0 with HNO3 as the supporting electrolyte, SCGD coupled with an interference filter provided a detection limit of 7.76 μg/L for Tl in water samples, comparable to traditional SCGD-AES. Furthermore, the spiked recovery rate of Tl is quantitative in real water samples and comparable to the performance of inductive coupled plasma – optical emission spectrometry (ICP-OES).

Detection of the amitraz pesticide in bee wax by hyperspectral imaging

AbstractThis study addressed pesticide contamination in beeswax, explicitly focusing on detecting Amitraz using innovative spectroscopy quantification. Beeswax’s susceptibility to pesticides poses risks to colony health, prompting the need for efficient detection methods. Current offline techniques, notably gas chromatography-mass spectrometry (GC-MS), are accurate but financially burdensome. Honeybees’ significance as global pollinators underscores the need to protect colonies from pesticides. Colony collapse disorder, triggered by pesticide exposure, is a widespread threat. This study proposes a cost-effective spectroscopy method for online hive monitoring, addressing gaps in existing identification methods. A systematic laboratory approach assessed the pesticide detection limit, targeting Amitraz in beeswax through online point spectroscopy and leveraging the short-wave infrared (SWIR) spectral range enhanced sensitivity to pesticide-induced color changes. The model, combining methods, proved reliable for estimating amitraz contamination, significantly exceeding one ppm, using Analytical Spectral Devices (ASD) sensors. Statistical analysis included. The study demonstrates the effectiveness of the AIW/BIW ratio in identifying amitraz concentrations above 1 ppm, particularly in the SWIR spectral range. Statistical analysis revealed a significant correlation between the AIW/BIW ratio and Amitraz concentrations, with a coefficient of determination (R2) of 0.9976. The proposed model, integrating methods, emerges as a dependable means for estimating amitraz contamination in beeswax, especially at concentrations exceeding 1 ppm, as validated through ASD sensors. Comparative analysis highlights the financial constraints associated with GC-MS and the impracticality of FTIR for online hive monitoring.

Non-planar graphene directly synthesized on intracavity optical microresonators for GHz repetition rate mode-locked lasers

Generation of high-speed laser pulses is essential for sustaining today’s global, hyper-connected society. One approach for achieving high spectral and temporal purity is to combine optical nonlinear materials with spectral filtering devices. In this work, a graphene-coated microresonator integrates a nonlinear material and a spectral filtering platform into a single device, creating a tunable GHz repetition rate mode-locked fiber laser. The graphene is directly synthesized on the non-planar surface of microresonator, resulting in a uniform, conformal coating with minimal optical loss in the device. The whispering gallery modes of the resonator filter the propagating modes, and the remaining modes from the interaction with graphene lock their relative phases to form short pulses at an elevated repetition rate relying on inter-modal spectral distance. Additionally, by leveraging the photo-thermal effect, all-optical tuning of the repetition rate is demonstrated. With optimized device parameters, repetition rates of 150 GHz and tuning of 6.1 GHz are achieved.

Bionic vine-stems structure for enhancing electrochromic cyclic stability and mechanical stability of multiband spectral regulation fiber

Wearable electrochromic (EC) devices can adjust visible-infrared spectra demonstrate promising potential for applications in display, decoration, personal thermal management, and adaptive thermal camouflage. However, existing flat layer-layer EC devices are hindered by poor air permeability, limited flexibility, and integration difficulties with fabrics, particularly in the realm of intelligent wearables. The creation of highly efficient fiber-shaped infrared (IR) modulation devices with exceptional structural and performance stability represents a significant challenge. We draw inspiration from the arboreous vine-stems structure observed in Chinese wistaria, which effectively minimizes the risk of damage to their vine and stems during harsh weather conditions. We have successfully designed a wearable high efficient multiband spectral regulation fiber (MSRF) with a bioinspired vine-stems structure using carbon fiber and polyaniline (PANI) as the active layer. The planar working electrode (Au) with PANI deposited on its surface was spirally wound around the counter electrode (carbon fiber) and separated by a gel electrolyte (PC/LiClO4), with polyolefin serving as the encapsulation layer. The distribution of electrical potential and lithium ions in the MSRF were analyzed using finite element analysis, demonstrating exceptional electrochromic stability without the length limit of the fiber. Moreover, the MSRF exhibited exceptional IR emissivity (△ε = 0.528) within the 8–14 μm range, fast response time (2.44 s) at low applied potentials (−0.8 V and 0.8 V), robust long-term cycling stability (at least 5000 cycles), and remarkable mechanical stability (at least 5000 bending cycles). The compatibility of the fiber with fabric and potential for patterning using MSRF with high length was also explored, providing new insights into the development of wearable adaptive thermal camouflage and spectral regulation fiber devices.

Metasurfaces based terahertz and mid- and long-wave infrared high-efficiency beam splitting devices

The multi-mode composite imaging technology integrates the advantages of different sensors, and thus has the advantages of high image quality, strong information acquisition capability, high target detection and recognition ability, strong adaptability to complex environments, and high stability and robustness of the system. Among them, the terahertz and infrared composite imaging technology combines the characteristics of terahertz band and infrared band, has the advantages of wide spectrum coverage, high resolution and strong penetration, and has broad application prospects. As one of the key components of the common aperture composite imaging system, the efficient optical splitters in terahertz and infrared band are still lacking at present, and their performance needs to be improved urgently. In this paper, a kind of dichroic metasurface with a simple structure and high performance is proposed by combining simulation experiment and theoretical explanation. When used as a spectroscopic device at an incident angle of 45°, it achieves a transmission coefficient greater than 97% near the center frequency of 1.1 THz, and a reflection coefficient greater than 98% in a wavelength range of 3–5 μm for medium-wave infrared and 8–14 μm for long-wave infrared. The design has good robustness to structural mismatches and machining errors such as structural misalignment, structural fillet, small magnification scaling, and polarization insensitivity. When the incident angle changes in a range of 0–60°, the device still maintains excellent spectral characteristics. In this paper, based on Babinet theorem and equivalent circuit model, the electromagnetic response characteristics of the metasurface are analyzed theoretically, and the analysis results are in agreement with the simulation results. The results of this study prove the feasibility of metasurface as a spectral device in the multiwavelength composite imaging system of terahertz and infrared bands, and provide support for future studying new composite imaging detection technology. In addition, the metasurface structure described in this paper has broad application prospects in many fields such as multi-band infrared stealth, laser and pump light separation in lasers, and provides a valuable reference for designing terahertz and infrared spectroscopy in various scenarios. In the following figure, for S wave and P wave at an incident angle of 45°, panel (a) shows the reflection coefficients varying with the wavelength of metasurface and panel (b) displays terahertz transmission coefficient changing with frequency.