Detecting natural gas storage microleakage based on K-means clustering under constraint of Jeffries-Matusita distance criterion using mobile LiDAR data.

Natural gas leaks alter both the spectral reflectance and the structure of surface vegetation, which can be used to indirectly monitor microleakages in gas storage facilities. However, existing methods predominantly focus on the spectral rather than structural response of stressed vegetation, and it is not clear whether structure characteristic can be used to identify natural gas stressed vegetation. In this study, the utility of mobile LiDAR in detecting vegetation structure changes due to natural gas stress was demonstrated by analyzing LiDAR data from a field experiment with bean and grass plants in their growing phase. A method utilizing the Jeffries-Matusita distance criterion constrained K-means clustering (JCKC) algorithm was proposed, which comprises three main steps: First, response of vegetation structure characteristic to natural gas stress was quantitatively analyzed at plot and pixel scales using LiDAR data. Second, the optimal set of structure characteristic parameters indicating natural gas stressed vegetation was determined using hierarchical clustering algorithm. Third, the reduced LiDAR data was clustered using K-means algorithm, and the clusters were classified under constraint of Jeffries-Matusita distance criterion to identify stressed vegetation. The results indicated natural gas stress significantly changes vegetation structure (p = 0.05), decreasing parameters like height, projected leaf area, canopy relief ratio, coefficient of variation of vegetation height, and entropy, while increasing homogeneity, contrast, and dissimilarity. The set of structure characteristic parameters based on height, homogeneity, and contrast can stably indicate natural gas stress, with Jeffries-Matusita distance values for comparing healthy and stressed vegetation samples exceeding 1.8. The proposed model achieved pixel-level identification accuracies of 98.95% for bean and 96.22% for grass, with average localization accuracies of 0.15 m and 0.12 m, respectively. This study demonstrates the potential of vegetation's structure characteristic in reflecting response to natural gas stress and monitoring natural gas storage microleakage in vegetated areas.

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.

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.

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.

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.

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.

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.

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.

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.

Gate-Tunable Magnetism via Resonant Se-Vacancy Levels in WSe2.

The confined defects in 2D van der Waals (vdW)-layered semiconductors can be easily tailored using charge doping, strain, or an electric field. Nevertheless, gate-tunable magnetic order via intrinsic defects has been rarely observed to date. Herein, a gate-tunable magnetic order via resonant Se vacancies in WSe2 is demonstrated. The Se-vacancy states are probed via photocurrent measurements with gating to convert unoccupied states to partially occupied states associated with photo-excited carrier recombination. The magneto-photoresistance hysteresis is modulated by gating, which is consistent with the density functional calculations. The two energy levels associated with Se vacancies split with increasing laser power, owing to the robust Coulomb interaction and strong spin-orbit coupling. The findings offer a new approach for controlling the magnetic properties of defects in optoelectronic and spintronic devices using vdW-layered semiconductors.

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.

Ultrashort Vertical-Channel van der Waals Semiconductor Transistors.

Atomically thin 2D van der Waals semiconductors are promising candidates for next-generation nanoscale field-effect transistors (FETs). Although large-area 2D van der Waals materials have been successfully synthesized, such nanometer-length-scale devices have not been well demonstrated in 2D van der Waals semiconductors. Here, controllable nanometer-scale transistors with a channel length of ≈10 nm are fabricated via vertical channels by squeezing an ultrathin insulating spacer between the out-of-plane source and drain electrodes, and the feasibility of high-density and large-scale fabrication is demonstrated. A large on-current density of ≈70 µA µm-1 nm-1 at a source-drain voltage of 0.5 V and a high on/off ratio of ≈107-109 are obtained in ultrashort 2D vertical channel FETs with monolayer MoS2 synthesized through chemical vapor deposition. The work provides a promising route toward the complementary metal-oxide-semiconductor-compatible fabrication of wafer-scale 2D van der Waals transistors with high-density integration.

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.

Efficient Gate Modulation in a Screening-Engineered MoS2/Single-Walled Carbon Nanotube Network Heterojunction Vertical Field-Effect Transistor.

In this report, a screening-engineered carbon nanotube (CNT) network/MoS2/metal heterojunction vertical field effect transistor (CNT-VFET) is fabricated for an efficient gate modulation independent of the drain voltage. The gate field in the CNT-VFET transports through the empty space of the CNT network without any screening layer and directly modulates the MoS2 semiconductor energy band, while the gate field from the Si back gate is mostly screened by the graphene layer. Consequently, the on/off ratio of CNT-VFET maintained 103 in overall drain voltages, which is 10 times and 1000 times higher than that of the graphene (Gr) VFET at Vsd = 0.1 (ratio = 81.9) and 1 V (ratio = 3), respectively. An energy band diagram simulation shows that the Schottky barrier modulation of CNT/MoS2 contact along the sweeping gate bias is independent of the drain voltage. On the other hand, the gate modulation of Gr/MoS2 is considerably reduced with increased drain voltage because more electrons are drawn into the graphene electrode and screens the gate field by applying a higher drain voltage to the graphene/MoS2/metal capacitor.

Temperature-Dependent Opacity of the Gate Field Inside MoS2 Field-Effect Transistors.

The transport behaviors of MoS2 field-effect transistors (FETs) with various channel thicknesses are studied. In a 12 nm thick MoS2 FET, a typical switching behavior is observed with an Ion/Ioff ratio of 106. However, in 70 nm thick MoS2 FETs, the gating effect weakens with a large off-current, resulting from the screening of the gate field by the carriers formed through the ionization of S vacancies at 300 K. Hence, when the latter is dual-gated, two independent conductions develop with different threshold voltage (VTH) and field-effect mobility (µFE) values. When the temperature is lowered for the latter, both the ionization of S vacancies and the gate-field screening reduce, which revives the strong Ion/Ioff ratio and merges the two separate channels into one. Thus, only one each of VTH and µFE are seen from the thick MoS2 FET when the temperature is less than 80 K. The change of the number of conduction channels is attributed to the ionization of S vacancies, which leads to a temperature-dependent intra- and interlayer conductance and the attenuation of the electrostatic gate field. The defect-related transport behavior of thick MoS2 enables us to propose a new device structure that can be further developed to a vertical inverter inside a single MoS2 flake.

Self-selective van der Waals heterostructures for large scale memory array.

The large-scale crossbar array is a promising architecture for hardware-amenable energy efficient three-dimensional memory and neuromorphic computing systems. While accessing a memory cell with negligible sneak currents remains a fundamental issue in the crossbar array architecture, up-to-date memory cells for large-scale crossbar arrays suffer from process and device integration (one selector one resistor) or destructive read operation (complementary resistive switching). Here, we introduce a self-selective memory cell based on hexagonal boron nitride and graphene in a vertical heterostructure. Combining non-volatile and volatile memory operations in the two hexagonal boron nitride layers, we demonstrate a self-selectivity of 1010 with an on/off resistance ratio larger than 103. The graphene layer efficiently blocks the diffusion of volatile silver filaments to integrate the volatile and non-volatile kinetics in a novel way. Our self-selective memory minimizes sneak currents on large-scale memory operation, thereby achieving a practical readout margin for terabit-scale and energy-efficient memory integration.

Defect-Affected Photocurrent in MoTe2 FETs.

Imperfections in the crystal lattice, such as defects, grain boundaries, or dislocations, can significantly affect the optical and electrical transport properties of materials. In this study, we report the effect of mid gap trap states on photocurrent in 10 atomic layered 2H-MoTe2. Our study reveals that the photocurrent is very sensitive to the number of active traps, which can be controlled by Vgs. By fitting the measured transient drain current, our estimation shows that the trap-state density is approximately 5 × 1011 cm-2. By analyzing the photocurrent data as a function of the gate voltage, we realize how the ionized traps affect the photoexcited carriers. The model of hole traps, electron traps, and recombination centers inside the band gap successfully describes our observed results.

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.