High-Spatial-Resolution NDVI Reconstruction with GA-ANN.

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm-artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space-time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction's NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.

Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device.

Since the first successful exfoliation of graphene, the superior physical and chemical properties of two-dimensional (2D) materials, such as atomic thickness, strong in-plane bonding energy and weak inter-layer van der Waals (vdW) force have attracted wide attention. Meanwhile, there is a surge of interest in novel physics which is absent in bulk materials. Thus, vertical stacking of 2D materials could be critical to discover such physics and develop novel optoelectronic applications. Although vdW heterostructures have been grown by chemical vapor deposition, the available choices of materials for stacking is limited and the device yield is yet to be improved. Another approach to build vdW heterostructure relies on wet/dry transfer techniques like stacking Lego bricks. Although previous reviews have surveyed various wet transfer techniques, novel dry transfer techniques have been recently been demonstrated, featuring clean and sharp interfaces, which also gets rid of contamination, wrinkles, bubbles formed during wet transfer. This review summarizes the optimized dry transfer methods, which paves the way towards high-quality 2D material heterostructures with optimized interfaces. Such transfer techniques also lead to new physical phenomena while enable novel optoelectronic applications on artificial vdW heterostructures, which are discussed in the last part of this review.

Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images.

The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.

Coherent Thermoelectric Power from Graphene Quantum Dots.

The quantum confinement of charge carriers has been a promising approach to enhance the efficiency of thermoelectric devices, by lowering the dimension of materials and raising the boundary phonon scattering rate. The role of quantum confinement in thermoelectric efficiency has been investigated by using macroscopic device-scale measurements based on diffusive electron transport with the thermal de Broglie wavelength of the electrons. Here, we report a new class of thermoelectric operation originating from quasi-bound state electrons in low-dimensional materials. Coherent thermoelectric power from confined charges was observed at room temperature in graphene quantum dots with diameters of several nanometers. The graphene quantum dots, electrostatically defined as circular n-p-n junctions to isolate charges in the p-type graphene quantum dots, enabled thermoelectric microscopy at the atomic scale, revealing weakly localized and coherent thermoelectric power generation. The conceptual thermoelectric operation provides new insights, selectively enhancing coherent thermoelectric power via resonant states of charge carriers in low-dimensional materials.

Synaptic Computation Enabled by Joule Heating of Single-Layered Semiconductors for Sound Localization.

Synaptic computation, which is vital for information processing and decision making in neural networks, has remained technically challenging to be demonstrated without using numerous transistors and capacitors, though significant efforts have been made to emulate the biological synaptic transmission such as short-term and long-term plasticity and memory. Here, we report synaptic computation based on Joule heating and versatile doping induced metal-insulator transition in a scalable monolayer-molybdenum disulfide (MoS2) device with a biologically comparable energy consumption (∼10 fJ). A circuit with our tunable excitatory and inhibitory synaptic devices demonstrates a key function for realizing the most precise temporal computation in the human brain, sound localization: detecting an interaural time difference by suppressing sound intensity- or frequency-dependent synaptic connectivity. This Letter opens a way to implement synaptic computing in neuromorphic applications, overcoming the limitation of scalability and power consumption in conventional CMOS-based neuromorphic devices.

Identification of fungi-contaminated peanuts using hyperspectral imaging technology and joint sparse representation model.

Peanuts with fungal contamination may contain aflatoxin, a highly carcinogenic substance. We propose the use of hyperspectral imaging to quickly and noninvasively identify fungi-contaminated peanuts. The spectral data and spatial information of hyperspectral images were exploited to improve identification accuracy. In addition, successive projection was adopted to select the bands sensitive to fungal contamination. Furthermore, the joint sparse representation based classification (JSRC), which considers neighboring pixels as belonging to the same class, was adopted, and the support vector machine (SVM) classifier was used for comparison. Experimental results show that JSRC outperforms SVM regarding robustness against random noise and considering pixels at the edge of the peanut kernel. The classification accuracy of JSRC reached 99.2% and 98.8% at pixel scale, at least 98.4% and 96.8% at kernel scale for two peanut varieties, retrieving more accurate and consistent results than SVM. Moreover, fungi-contaminated peanuts were correctly marked in both learning and test images.

[Use of Near-Infrared Hyperspectral Images to Differentiate Architectural Coatings with Different Qualities].

Architectural coatings sold in market fall into many categories which mean different models and qualities. The research plans to differentiate different kinds of architectural coatings in quality using hyperspectral technology. Near-Infrared hyperspectral images of four kinds of architectural coatings (in a descending quality order of brand A, B, C, and D) in same color were acquired. The optimal wavelengths were selected at 1283 and 2447 nm to differentiate the four kinds of coatings through ANOVA (Analysis of Variance) method. The band ratio index of R1283/R2447 was built and the results were segmented into the corresponding coatings, and the accuracies of segmentation were compared with that from Maximum Likely Classification (MLC). The results indicated all J-M distances are more than 1.8 except between C and D; the lowest accuracy of 87.54% in segmentation and 95.63% in MLC were both from brand D, and others' accuracies all were over 90% in both ratio index and MLC. Therefore, the ratio index R1283/R2447 could be used to distinguish different kinds of architectural coatings. Also, the research could provide support for identification, quality acceptance, as well as conformity assessment of architectural coatings.

[Study on the Detection of Slight Mechanical Injuries on Apples with Hyperspectral Imaging].

Nondestructive detection is one of the hottest spots in the application of hyperspectral remote sensing. The apple is easy to produce slight mechanical injuries that affects its quality in the process of picking and transporting. The hyperspectral images of 54 "yellow banana" and "Yantai Fushi" apples with slight injuries in the visible and near-infrared (400~1 000 nm) ranges are acquired; the mean spectral curves of injury regions on apples are extracted; the endmember spectrum are extracted based on minimum noise fraction (MNF) and geometric vertex principle; and the spectral angle is calculated between spectral of injury region and endmember spectral; a model of endmember extraction spectral angle (EESA) is constructed to detect slight mechanical injuries on apples. The slight mechanical injuries on "yellow banana" and "Yantai Fushi" apples are detected by setting spectral angle threshold, and the detection accuracy is compared with MNF and principal component analysis (PCA) method. The results show that the accuracy of EESA model is the highest, and the detection accuracy rate reaches 94.44% and 90.07% respectively.

[Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat Based on Wavelet Feature].

Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350～1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer' accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user' accuracy and producer' accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image.

[Simulated Experimental Research on Using Canopy Spectra of Surface Vegetation to Detect CO2 Microseepage Spots].

With the global warming, people now pay more attention to the problem of the emission of greenhouse gas (CO2). Carbon capture and storage (CCS) technology is an effective measures to reduce CO2 emission. But there is a possible risk that the CO2 might leak from underground. However, there need to research and develop a technique to quickly monitor CO2 leaking spots above sequestration fields. The field experiment was performed in the Sutton Bonington campus of University of Nottingham (52. 8N, 1. 2W) from May to September in 2008. The experiment totally laid out 16 plots, grass (cv Long Ley) and beans (Vicia faba cv Clipper) were planted in eight plots, respectively. However, only four grass and bean plots were stressed by the CO2 leakage, and CO2 was always injected into the soil at a rate of 1 L x min(-1). The canopy spectra were measured using ASD instrument, and the grass was totally collected 6 times data and bean was totally collected 3 times data. This paper study the canopy spectral characteristics of grass and beans under the stress of CO2 microseepages through the field simulated experiment, and build the model to detect CO2 microseepage spots by using hyperspectral remote sensing. The results showed that the canopy spectral reflectance of grass and beans under the CO2 leakage stress increased in 580-680 nm with the stressed severity elevating, moreover, the spectral features of grass and beans had same rule during the whole experimental period. According to the canopy spectral features of two plants, a new index AREA(5800680 nm) was designed to detect the stressed vegetations. The index was tested through J-M distance, and the result suggested that the index was able to identify the center area and the core area grass under CO2 leakage stress, however, the index had a poor capability to discriminate the edge area grass from control. Then, the index had reliable and steady ability to identify beans under CO2 leakage stress. This result could provide theoretical basis and methods for detecting CO2 leakage spots using hyperspectral remote sensing in the future.

[Comparison and analysis of hyperspectral remote sensing identifiable models for different vegetation under waterlogging stress].

With the global climate warming, flooding disasters frequently occurred and its influence scope constantly increased in China. The objective of the present paper was to study the leaf spectral features of vegetation (maize and beetroot) under waterlogging stress and design a hyperspectral remote sensing model to monitor the flooding disasters through a field simulated experiment. The experiment was carried out in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1. 2 degrees W) from May to August in 2008, and samples were collected one time every week and spectra were measured in the laboratory. The result showed that the reflectance of the maize and beetroot decreased in the 550 and 800-1 300 nm region, and the reflectance slightly increased in the 680 nm region. This paper chose NDVI, SIPI, PRI, SRPI, GNDVI and R800 * R550/R680 to identify the vegetation under waterlogging stress, respectively. The result suggested that the SIPI and R800 * R550/R680 was sensitive for maize under waterlogging stress, and then SIPI and PRI and R800 * R550/R680 was sensitive for beetroot under waterlogging stress. In order to seek the best identifiable model, the normalized distances between means of control and stressed vegetation indices were calculated and analyzed, the result indicated that the distance of R800 * R550/R680 is more than that of indices' in the early stress stage, illustrated that the index identifiable ability for waterlogging stress is better than other indices, then the index has the strong sensitivity and stability. Therefore, the index R800 * R550/R680 could be used to quickly extract flooding disaster area by using hyperspectral remote sensing, and would provide information support for disaster relief decisions.

Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities.

Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.

Reversible Amorphous-Crystalline Phase Transformation in an Ultrathin van der Waals FeTe System.

Searching for new phase-change materials for memory and neuromorphic device applications and further understanding the phase transformation mechanism are attracting wide attention. Phase transformation from the amorphous phase to the crystal phase has been unraveled in iron telluride (FeTe) bulk film deposited by pulsed laser deposition, recently. However, the van der Waals-layered feature of FeTe in the crystal form was not noted, which will benefit the scaling of the memory devices and shine light on phase-change heterostructures or interfacial phase-change materials. Moreover, the demonstration of advanced memory or neuromorphic device applications is lacking. Here, we investigate the phase transformation of FeTe starting from mechanically exfoliated van der Waals layers from a bulk single crystal. Surficial amorphization is revealed at the surface layers of FeTe flakes after exfoliation under ambient conditions, which could be transformed back to the crystalline phase with laser irradiation or heating. The conductance drop of the flake devices near 400 K verifies the phase transformation electrically. Memristor behavior of the amorphous surface in FeTe has been further demonstrated, proving the reversibility of the phase transformation and shining light on the possible applications of neuromorphic devices.

Silicon-Based On-Chip Tunable High-Q-Factor and Low-Power Fano Resonators with Graphene Nanoheaters.

Tunable and low-power microcavities are essential for large-scale photonic integrated circuits. Thermal tuning, a convenient and stable tuning method, has been widely adopted in optical neural networks and quantum information processing. Recently, graphene thermal tuning has been demonstrated to be a power-efficient technique, as it does not require thick spacers to prevent light absorption. In this paper, a silicon-based on-chip Fano resonator with graphene nanoheaters is proposed and fabricated. This novel Fano structure is achieved by introducing a scattering block, and it can be easily fabricated in large quantities. Experimental results demonstrate that the resonator has the characteristics of a high quality factor (∼31,000) and low state-switching power (∼1 mW). The temporal responses of the microcavity exhibit qualified modulation speed with 9.8 µs rise time and 16.6 µs fall time. The thermal imaging and Raman spectroscopy of graphene at different biases were also measured to intuitively show that the tuning is derived from the joule heating effect of graphene. This work provides an alternative for future large-scale tunable and low-power-consumption optical networks, and has potential applications in optical filters and switches.

[Spectral characteristics and identification research of soybean under different disease stressed].

The objective of this paper is to identify disease and its severity of soybean by using single leaf spectral data in the field. The soybean spectral were measured in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1.2 degrees W), which infected rust disease (RD) and common mosaic disease (CMD), respectively, and continuum removal method was used to process the original spectral data, and sensitive bands were selected for disease and disease severity, and vegetation index was designed for identifying RD and CMD of soybean. The result showed spectral reflectance of soybean under CMD stressed is more than that of health in the visible region. However, spectral reflectance of soybean under RD stressed will decrease in the green region and that will increase in the red region with disease severity increasing. According to the spectral changing features, a new index R500 x R550/R680 was designed for identifying the disease of soybean. In order to test the index identifying disease ability, the J-M distances were calculated among health, RD and CMD. The result indicated index R500 x R550/R680 can better identify RD and CMD, at the same time, the index has good ability for discriminating the disease severity of soybean. The research results of this paper has important theoretical value for crops disease monitoring and prevention and practical application meanings.

[Research on the spectral feature and identification of the surface vegetation stressed by stored CO2 underground leakage].

With the global climate warming, reducing greenhouse gas emissions becomes a focused problem for the world. The carbon capture and storage (CCS) techniques could mitigate CO2 into atmosphere, but there is a risk in case that the CO2 leaks from underground. The objective of this paper is to study the chlorophyll contents (SPAD value), relative water contents (RWC) and leaf spectra changing features of beetroot under CO2 leakage stress through field experiment. The result shows that the chlorophyll contents and RWC of beetroot under CO2 leakage stress become lower than the control beetroot', and the leaf reflectance increases in the 550 nm region and decreases in the 680nm region. A new vegetation index (R550/R680) was designed for identifying beetroot under CO2 leakage stress, and the result indicates that the vegetation index R550/R680 could identify the beetroots after CO2 leakage for 7 days. The index has strong sensitivity, stability and identification for monitoring the beetroots under CO2 stress. The result of this paper has very important meaning and application values for selecting spots of CCS project, monitoring and evaluating land-surface ecology under CO2 stress and monitoring the leakage spots by using remote sensing.

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies.

Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango.

Coordination Study on Ecological and Economic Coupling of the Yellow River Basin.

The coupling and coordination relationship between ecology and the economy in the Yellow River Basin is a hot topic in sustainable development research. Said research has important guiding significance for the ecological security and comprehensive development of the Yellow River Basin. Taking the Yellow River Basin as the object of our study, based on the data of the economy, energy consumption data, ecology data and water resources data, we construct an indicator system of the economic development and ecological status of the Yellow River Basin and use the principal component analysis method to calculate the economic development and ecological status index. Then, we use the evaluation method, the coupling degree model and the coupling coordination degree model to analyze the time and space evolution trends of economic development and ecological state, coupling degree and coupling coordination degree. The results show that: (1) From 2000 to 2018, the economic development index of the Yellow River Basin rose steadily; the ecological status index showed a slow rise and then a downward trend. (2) The degree of coupling between economic development and ecological state has been considered as intensity coupling after 2005. The coupling trend slowly increased and then decreased, indicating that the interaction effect between the economy and ecology was first significantly enhanced and then slowly weakened. (3) The degree of coupling coordination increased from 0.2994 to 0.6266 and then decreased to 0.5917, reflecting the continuous improvement of the relationship between the regional economy and the ecological environment and the trend toward coordination. From 2015 to 2018, due to the gradual increase in the difference between economic development and ecological conditions, the coupling and coordination between the two decreased. Studies have shown that ecological construction and protection should be strengthened to ease the contradiction between the economy and ecology and achieve coordinated development.

In-sensor reservoir computing for language learning via two-dimensional memristors.

The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.

Fabrication of 1D Te/2D ReS2 Mixed-Dimensional van der Waals p-n Heterojunction for High-Performance Phototransistor.

The superior optical and electronic properties of the two-dimensional (2D) rhenium disulfide (ReS2) makes it suitable for nanoelectronic and optoelectronic applications. However, the internal defects coupled with with the low mobility and light-absorbing capability of ReS2 impede its utilization in high-performance photodetectors. Fabrication of mixed-dimensional heterojunctions is an alternative method for designing high-performance hybrid photodetectors. This study proposes a mixed-dimensional van der Waals (vdW) heterojunction photodetector, containing high-performance one-dimensional (1D) p-type tellurium (Te) and 2D n-type ReS2, developed by depositing Te nanowires on ReS2 nanoflake using the dry transfer method. It can improve the injection and separation efficiency of photoexcited electron-hole pairs due to the type II p-n heterojunction formed at the ReS2 and Te interface. The proposed heterojunction device is sensitive to visible-light sensitivity (632 nm) with an ultrafast photoresponse (5 ms), high responsivity (180 A/W), and specific detectivity (109), which is superior to the pristine Te and ReS2 photodetectors. As compared to the ReS2 device, the responsivity and response speed is better by an order of magnitude. These results demonstrate the fabrication and application potential of Te/ReS2 mixed-dimensional heterojunction for high-performance optoelectronic devices and sensors.