PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Heteronuclear Dual Metal Atom Electrocatalysts for Water-Splitting Reactions.

Hydrogen is considered a promising substitute for traditional fossil fuels because of its widespread sources, high calorific value of combustion, and zero carbon emissions. Electrocatalytic water-splitting to produce hydrogen is also deemed to be an ideal approach; however, it is a challenge to make highly efficient and low-cost electrocatalysts. Single-atom catalysts (SACs) are considered the most promising candidate to replace traditional noble metal catalysts. Compared with SACs, dual-atom catalysts (DACs) are capable of greater attraction, including higher metal loading, more versatile active sites, and excellent catalytic activity. In this review, several general synthetic strategies and structural characterization methods of DACs are introduced, and recent experimental advances in water-splitting reactions are discussed. The authors hope that this review provides insights and inspiration to researchers regarding DACs in electrocatalytic water-splitting.

Molybdenum and Vanadium-Codoped Cobalt Carbonate Nanosheets Deposited on Nickel Foam as a High-Efficient Bifunctional Catalyst for Overall Alkaline Water Splitting.

To address issues of global energy sustainability, it is essential to develop highly efficient bifunctional transition metal-based electrocatalysts to accelerate the kinetics of both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Herein, the heterogeneous molybdenum and vanadium codoped cobalt carbonate nanosheets loaded on nickel foam (VMoCoCOx@NF) are fabricated by facile hydrothermal deposition. Firstly, the mole ratio of V/Mo/Co in the composite is optimized by response surface methodology (RSM). When the optimized composite serves as a bifunctional catalyst, the water-splitting current density achieves 10 mA cm-2 and 100 mA cm-2 at cell voltages of 1.54 V and 1.61 V in a 1.0 M KOH electrolyte with robust stability. Furthermore, characterization is carried out using field emission scanning electron microscopy-energy dispersive spectroscopy (FESEM-EDS), high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Density functional theory (DFT) calculations reveal that the fabricated VMoCoCOx@NF catalyst synergistically decreases the Gibbs free energy of hydrogen and oxygen-containing intermediates, thus accelerating OER/HER catalytic kinetics. Benefiting from the concerted advantages of porous NF substrates and clustered VMoCoCOx nanosheets, the fabricated catalyst exhibits superior electrocatalytic performance. This work presents a novel approach to developing transition metal catalysts for overall water splitting.

Ta3N5 Nanobelt-Loaded Ru Nanoparticle Hybrids' Electrocatalysis for Hydrogen Evolution in Alkaline Media.

Electrochemical hydrogen evolution is a highly efficient way to produce hydrogen, but since it is limited by high-cost electrocatalysts, the preparation of high-efficiency electrocatalysts with fewer or free noble metals is important. Here, Ta3N5 nanobelt (NB)-loaded Ru nanoparticle (NP) hybrids with various ratios, including 1~10 wt% Ru/Ta3N5, are constructed to electrocatalyze water splitting for a hydrogen evolution reaction (HER) in alkaline media. The results show that 5 wt% Ru/Ta3N5 NBs have good HER properties with an overpotential of 64.6 mV, a Tafel slope of 84.92 mV/dec at 10 mA/cm2 in 1 M of KOH solution, and good stability. The overpotential of the HER is lower than that of Pt/C (20 wt%) at current densities of 26.3 mA/cm2 or more. The morphologies and structures of the materials are characterized by scanning electron microscopy and high-resolution transmission electron microscopy, respectively. X-ray photoelectron energy spectroscopy (XPS) demonstrates that a good HER performance is generated by the synergistic effect and electronic transfer of Ru to Ta3N5. Our electrochemical analyses and theoretical calculations indicate that Ru/Ta3N5 interfaces play an important role as real active sites.

Synthesis and solar blind photosensitivity of crystalline boron nanowires.

Herein, single crystalline boron nanowires (BNWs) have been synthesized by chemical vapor transport using boron element as boron source, iodine as transport agent, and Au as catalyst. The results demonstrate that BNWs can be all formed at 600 °C-950 °C for 2 h, and possess rhombohedral crystal structure (ß-boron). The NWs have diameters from several to hundreds of nanometers, and lengths from several to hundreds of microns. A single nanowire has been fabricated to field effect transistor (FET) which shows excellent solar blind photosensitivity and selectivity. The photo/dark current ratio and photoresponsitity is 1.14 and 97.6 mA W-1at a bias of 5 V under light illumination of 254 nm with 0.42 mW cm-2, respectively, and both the rising and decay time of the on-off currents are 4.6 s and 10.3 s, respectively. When the FET is used as a personal breath sensor, the ratio of exsufflating and inhaling currents is 2.7, rising and decay time of the breath currents are 0.4 s and 2.2 s, respectively. So the BNWs are important sense materials.

Plasmonic Modulation of the Upconversion Luminescence Based on Gold Nanorods for Designing a New Strategy of Sensing MicroRNAs.

Upconversion nanoparticles (UCNPs) have potential applications in biosensing and bioimaging. However, the UCNPs-based sensors constructed by luminescence resonance energy transfer (LRET) always suffer from low quenching efficiency, hindering their application. Therefore, exploring a new strategy to resolve this issue is highly desirable. Herein, a strategy based on the surface plasmon resonance (SPR) effect of gold nanorods (AuNRs) is presented. The luminescence of UCNPs was modulated by adjusting the SiO2 thickness of AuNRs@SiO2 and the structure of UCNPs; an enhancement factor of ≈50 times was obtained. Based on the results of the SPR effect of AuNRs, we designed two kinds of potential upconversion microRNA sensors using microRNA-21 as a model to resolve the problem of the lower quenching efficiency resulting from a dye as a quencher. Studies revealed that the proposed strategy could be successfully used to construct upconversion microRNA sensors for avoiding the limitation of the low quenching efficiency. The sensitivity was ≈10 000 times higher than that of the upconversion sensor using dyes as quenchers. Importantly, the assay of microRNA-21 was successfully achieved using this sensor in human serum samples and human breast cancer cell (MCF-7) lysates. It provides a new method for designing upconversion microRNA sensors and may have potential for use in biosensing and bioimaging.

Ag nanoparticles decorated urchin-like cobalt carbonate hydroxide composites for highly efficient oxygen evolution reaction.

Herein, a novel composite of small amounts of Ag nanoparticles (NPs) decorated urchin-like cobalt carbonate hydroxide hydrate (CCHH) was developed for highly-efficient alkaline oxygen evolution reaction (OER). Not only can Ag colloids, as template agents, modify the morphologies of urchin-like CCHH microspheres to expose more active sites available, but also the supported Ag NPs formed by Ag colloids can transfer the electron to CCHH surfaces, accelerating the transformation of surface CoII to CoIII/CoIV (proton-coupled electron transfer (PCET) process). The urchin-like Ag/CCHH (0.013 mmol) precatalyst (before cyclic voltammetry (CV) activation) exhibits a better OER performance (a low overpotential of 273 mV at 10 mA cm-2 and small Tafel slope of 65 mV dec-1) as compared with commercial RuO2. Furthermore, the dynamic surface self-reconstruction (surface CO3 2- and OH - exchange) can further enhance the activities of Ag/CCHH precatalysts. Consequently, the optimal Ag/CCHH (0.013 mmol) catalyst presents a superior activity (a lower overpotential of 267 mV at 10 mA cm-2 and markedly reduced Tafel slope to 56 mV dec-1) along with an excellent stability after CV cycles. The study provides a feasible strategy to fully realize the low overpotential of CCHH-based OER electrocatalysts.

A Universal Upconversion Sensing Platform for the Sensitive Detection of Tumour-Related ncRNA through an Exo III-Assisted Cycling Amplification Strategy.

Here, a sensitive and universal noncoding RNA (ncRNA) upconversion sensing nanoplatform is developed. Gold nanoparticles bearing one hairpin DNA (Hp) molecule are conjugated to the linker DNA modified NaYF4 :Yb, Er@NaYF4 upconversion nanoparticles by DNA hybridization, leading to quenching of the upconversion emission through fluorescence resonance energy transfer. A signal DNA (SDNA) sequence is designed to open Hp, recovering the upconversion emission. To achieve universality and high sensitivity of the nanoprobe, an exonuclease III (Exo III)-assisted cycling amplification strategy is introduced. A multifunctional hairpin DNA (mHp) containing ncRNA recognition sequence and SDNA sequence is designed to recognize ncRNA and trigger Exo III as a biocatalyst to stepwise disintegrate itself, releasing both ncRNA and SDNA. The released ncRNA can be reused to release more SDNA, which greatly improves the sensing sensitivity. By changing the recognition portion of mHp, various ncRNA can be detected. The sensitive detection of both homeobox (HOX) transcript antisense RNA segment and miR-21 is achieved with this novel strategy, even in human serum, indicating the universality and sensitivity of the proposed strategy. Additionally, the expression level of miR-21 in human breast cancer cell (MCF-7) lysate is successfully measured, suggesting its potential in clinical diagnosis.

Lighting Up MicroRNA in Living Cells by the Disassembly of Lock-Like DNA-Programmed UCNPs-AuNPs through the Target Cycling Amplification Strategy.

Intracellular microRNAs imaging based on upconversion nanoprobes has great potential in cancer diagnostics and treatments. However, the relatively low detection sensitivity limits their application. Herein, a lock-like DNA (LLD) generated by a hairpin DNA (H1) hybridizing with a bolt DNA (bDNA) sequence is designed, which is used to program upconversion nanoparticles (UCNPs, NaYF4 @NaYF4 :Yb, Er@NaYF4 ) and gold nanoparticles (AuNPs). The upconversion emission is quenched through luminescence resonance energy transfer (LRET). The multiple LLD can be repeatedly opened by one copy of target microRNA under the aid of fuel hairpin DNA strands (H2) to trigger disassembly of AuNPs from the UCNP, resulting in the lighting up of UCNPs with a high detection signal gain. This strategy is verified using microRNA-21 as model. The expression level of microRNA-21 in various cells lines can be sensitively measured in vitro, meanwhile cancer cells and normal cells can be easily and accurately distinguished by intracellular microRNA-21 imaging via the nanoprobes. The detection limit is about 1000 times lower than that of the previously reported upconversion nanoprobes without signal amplification. This is the first time a nonenzymatic signal amplification method has been combined with UCNPs for imaging intracellular microRNAs, which has great potential for cancer diagnosis.

Visible light nonlinear absorption and optical limiting of ultrathin ZrSe3 nanoflakes.

The nonlinear absorption and nonlinear refractive properties of ZrSe3 nanoflakes were studied with a 6.5 ns pulse laser at 532 nm. Open-aperture Z-scan curves reveal that ZrSe3 nanoflakes have a strong reverse saturable absorption property, and close-aperture Z-scan curves show that ZrSe3 dispersions possess a positive nonlinear refractive index caused by self-focusing. The nonlinear absorption coefficient, the nonlinear refraction coefficient, and the figures of merit (FOM) of ZrSe3 dispersed in water with linear transmittances of 0.86 at input energy of 18 µJ are 6.35 × 10-10 m W-1 15.73 × 10-17 m2 W-1, and 10.09 × 10-11 esu · cm respectively. In addition, nonlinear optical (NLO) performance of ZrSe3 nanoflakes depends on organic solvent dispersions. ZrSe3 nanoflakes in water dispersions have the largest FOM of 10.27 × 10-11 esu · cm, while the FOM in ethanol dispersions is 5.41 × 10-11 esu · cm at the same input energy of 26.5 µJ. The optical limiting threshold Fth of ZrSe3 nanosheet is 2.2 J cm-2 under picosecond laser pulse. The Results imply that ZrSe3 nanoflakes are an extraordinarily promising material for novel nanophotonic devices like optical limiters.

An upconversion fluorescent resonant energy transfer biosensor for hepatitis B virus (HBV) DNA hybridization detection.

A novel fluorescent resonant energy transfer (FRET) biosensor was fabricated for the detection of hepatitis B virus (HBV) DNA using poly(ethylenimine) (PEI) modified upconversion nanoparticles (NH2-UCNPs) as energy donor and gold nanoparticles (Au NPs) as acceptor. The PEI modified upconversion nanoparticles were prepared directly with a simple one-pot hydrothermal method, which provides high quality amino-group functionalized UCNPs with uniform morphology and strong upconversion luminescence. Two single-stranded DNA strands, which were partially complementary to each other, were then conjugated with NH2-UCNPs and Au NPs. When DNA conjugated NH2-UCNPs and Au NPs are mixed together, the hybridization between complementary DNA sequences on UCNPs and Au NPs will lead to the quenching of the upconversion luminescence due to the FRET process. Meanwhile, upon the addition of target DNA, Au NPs will leave the surface of the UCNPs and the upconversion luminescence can be restored because of the formation of the more stable double-stranded DNA on the UCNPs. The sensor we fabricated here for target DNA detection shows good sensitivity and high selectivity, which has the potential for clinical applications in the analysis of HBV and other DNA sequences.

Histidine-modified organic-silica hybrid monolithic column for mixed-mode per aqueous and ion-exchange capillary electrochromatography.

A novel organic-silica hybrid monolith was prepared through the binding of histidine onto the surface of monolithic matrix for mixed-mode per aqueous and ion-exchange capillary electrochromatography. The imidazolium and amino groups on the surface of the monolithic stationary phase were used to generate an anodic electro-osmotic flow as well as to provide electrostatic interaction sites for the charged compounds at low pH. Typical per aqueous chromatographic behavior was observed in water-rich mobile phases. Various polar and hydrophilic analytes were selected to evaluate the characteristics and chromatographic performance of the obtained monolith. Under per aqueous conditions, the mixed-mode mechanism of hydrophobic and ion-exchange interactions was observed and the resultant monolithic column proved to be very versatile for the efficient separations of these polar and hydrophilic compounds (including amides, nucleosides and nucleotide bases, benzoic acid derivatives, and amino acids) in highly aqueous mobile phases. The successful applications suggested that the histidine-modified organic-silica hybrid monolithic column could offer a wide range of retention behaviors and flexible selectivities toward polar and hydrophilic compounds.

Flexible visible-light photodetectors with broad photoresponse based on ZrS3 nanobelt films.

Two new flexible visible-light photodetectors based on ZrS3 nanobelts films are fabricated on a polypropylene (PP) film and printing paper, respectively, by an adhesive-tape transfer method, and their light-induced electric properties are investigated in detail. The devices demonstrate a remarkable response to 405 to 780 nm light, a photocurrent that depends on the optical power and light wavelength, and an excellent photoswitching effect and stability. This implies that ZrS3 nanobelts are prospective candidates for high-performance nanoscale optoelectronic devices that may be practically applied in photodetection of visible to near infrared light. The facile fabrication method is extendable to flexible nanodevices with different nanostructures.

CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.

Wheat is the most widely grown crop in the world, and its yield is closely related to global food security. The number of ears is important for wheat breeding and yield estimation. Therefore, automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield. However, all existing methods require position-level annotation for training, implying that a large amount of labor is required for annotation, limiting the application and development of deep learning technology in the agricultural field. To address this problem, we propose a count-supervised multiscale perceptive wheat counting network (CSNet, count-supervised network), which aims to achieve accurate counting of wheat ears using quantity information. In particular, in the absence of location information, CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features. We conduct comparative experiments on a publicly available global wheat head detection dataset, showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error (MAE) and root mean square error (RMSE). This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs, demonstrating its great potential for agricultural counting tasks. The code is available at http://csnet.samlab.cn.

Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.

Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.

Gas sensors based on alpha-Fe2O3 nanorods, nanotubes and nanocubes.

Nanorods, nanotubes, and nanocubes of alpha-Fe2O3 were synthesized via a hydrothermal process and subsequent calcination treatment. The typical nanorod has about 71 nm in width, 7 nm in thickness, and 314 nm in length. The nanotube has outer diameter of 90-110 nm, inner diameter of 40-80 nm, and lengths of 250-400 nm. The nanocubes are rhombus cubes with an edge length of about 680 nm. The nanorods, nanotubes and nanocubes were fabricated into chemiresistive sensors. The responses (R(a)/R(g)) of the nanotube sensor to ethanol, acetone, formaldehyde, and ammonia are greatly higher than those of the nanorod/the nanocube sensors. The nanotubes showed enhancing gas-sensitive properties. The responses of the nanotube sensor reached 75, 26, 6.8, 1.7 to 200 ppm acetone, ethanol, formaldehyde, and ammonia at 270 degrees C and a 30-45% relative humidity, respectively, which shows high selectivity. So the nanotubes are gas-sensing materials more excellent than nanorods and nanocubes.

Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model.

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production, which benefits food production. Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases. However, existing methods are still limited to single crop disease diagnosis. More importantly, the existing model has a large number of parameters, which is not conducive to deploying it to agricultural mobile devices. Nonetheless, reducing the number of model parameters tends to cause a decrease in model accuracy. To solve these problems, we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops. In detail, we design 2 strategies to build 4 different lightweight models as student models: the YOLOR-Light-v1, YOLOR-Light-v2, Mobile-YOLOR-v1, and Mobile-YOLOR-v2 models, and adopt the YOLOR model as the teacher model. We develop a multistage knowledge distillation method to improve lightweight model performance, achieving 60.4% mAP@ .5 in the PlantDoc dataset with small model parameters, outperforming existing methods. Overall, the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy. Not only that, the technique can be extended to other tasks, such as image classification and image segmentation, to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture. Our code is available at https://github.com/QDH/MSKD.

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.

Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.

Preparation and field-emission of TaSe2 nanobelt quasi-arrays, and electrical transport of its individual nanobelt.

3R-TaSe2 nanobelt quasi-arrays were gown on a Ta foil by a facile two-step method, namely, firstly the TaSe3 nanobelt arrays were grown on a Ta foil by a surface-assisted chemical vapor transport, and then they were pyrolyzed to 3R-TaSe2 nanobelt quasi-arrays in vacuum. The nanobelts have low work function and the Ta foil has high conductivity, so the nanobelt arrays possess good electronic field emission performance with a low turn-on (3.6 V/microm) and threshold fields (4.3 V/microm) (which are defined as the macroscopic field required to produce a current density of 10 microA/cm2 and 1 mA/cm2, respectively) and a high enhancement factor (1045) at an emission distance of 200 microm. The electric transport of the individual nanobelt reveals that it is a high-conductive semiconductor, and observed by the variable-range hopping model. It suggests that the nanobelts have potential applications in field emission and field effect transistors.

NbN and NbS2 nanobelt arrays: in-situ conversion preparation and field-emission performance.

NbN nanobelt arrays were prepared by an in-situ conversion of aligned NbS3 nanobelts in the flowing NH3 at 950 degrees C, meanwhile, NbS2 nanobelt arrays were produced by a pyrolysis of aligned NbS3 nanobelts in the flowing argon atmosphere. The morphology and size of those nanobelts are close to the NbS3 nanobelt precursors, having a reactangular section of about 11 x 250 - 520 x 3100 nm2, and a length of about 120 microm. The microstructures were characterized by high-resolution transmission electron microscopy (HRTEM) and energy-dispersive X-ray spectroscopy (EDX). Magnetic measurements showed that the NbN nanobelts have superconductivity below 14.1 K. Field-emission experiments showed that the turn-on and threshold fields of the NbN nanobelt arrays (which are defined to be the macroscopic field to produce a current density of 10 microA/cm2 and 1 mA/cm2, respectively) are 0.61 and 2.3 V/microm, respectively, whereas the turn-on and threshold fields of the NbS2 nanobelt arrays are 0.61 and 3.5 V/microm, respectively, suggesting that they are decent field emitters.