Finite Difference-Peridynamic Differential Operator for Solving Transient Heat Conduction Problems

Transient heat conduction problems widely exist in engineering.In previous work on the peridynamic differential operator(PDDO)method for solving such problems,both time and spatial derivatives were discretized using the PDDO method,resulting in increased complexity and programming difficulty.In this work,the forward difference formula,the backward difference formula,and the centered difference formula are used to discretize the time derivative,while the PDDO method is used to discretize the spatial derivative.Three new schemes for solving transient heat conduction equations have been developed,namely,the forward-in-time and PDDO in space(FT-PDDO)scheme,the backward-in-time and PDDO in space(BT-PDDO)scheme,and the central-in-time and PDDO in space(CT-PDDO)scheme.The stability and convergence of these schemes are analyzed using the Fourier method and Taylor’s theorem.Results show that the FT-PDDO scheme is conditionally stable,whereas the BT-PDDO and CT-PDDO schemes are unconditionally stable.The stability conditions for the FT-PDDO scheme are less stringent than those of the explicit finite element method and explicit finite difference method.The convergence rate in space for these three methods is two.These constructed schemes are applied to solve one-dimensional and two-dimensional transient heat conduction problems.The accuracy and validity of the schemes are verified by comparison with analytical solutions.

Euler’s First-Order Explicit Method–Peridynamic Differential Operator for Solving Population Balance Equations of the Crystallization Process

Using Euler’s first-order explicit(EE)method and the peridynamic differential operator(PDDO)to discretize the time and internal crystal-size derivatives,respectively,the Euler’s first-order explicit method–peridynamic differential operator(EE–PDDO)was obtained for solving the one-dimensional population balance equation in crystallization.Four different conditions during crystallization were studied:size-independent growth,sizedependent growth in a batch process,nucleation and size-independent growth,and nucleation and size-dependent growth in a continuous process.The high accuracy of the EE–PDDO method was confirmed by comparing it with the numerical results obtained using the second-order upwind and HR-van methods.The method is characterized by non-oscillation and high accuracy,especially in the discontinuous and sharp crystal size distribution.The stability of the EE–PDDO method,choice of weight function in the PDDO method,and optimal time step are also discussed.

Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

Anti-Jamming Null Space Projection Beamforming Based on Symbiotic Radio

With the development of information technology,more and more devices are connected to the Internet through wireless communication to complete data interconnection.Due to the broadcast characteristics ofwireless channels,wireless networks have suffered more and more malicious attacks.Physical layer security has received extensive attention from industry and academia.MIMO is considered to be one of the most important technologies related to physical layer security.Through beamforming technology,messages can be transmitted to legitimate users in an offset direction that is as orthogonal as possible to the interference channel to ensure the reception SINR by legitimate users.Combining the symbiotic radio(SR)technology,this paper considers a symbiotic radio antijamming MIMO system equipped with a multi-antenna system at the main base station.In order to avoid the interference signal and improve the SINR of the signal received by the user.The base station is equipped with a uniform rectangular antenna array,and using Null Space Projection(NSP)Beamforming,Intelligent Reflecting Surface(IRS)can assist in changing the beam’s angle.The simulation results show that NSP Beamforming could make a better use of the null space of interference,which can effectively improve the received SINR of users under directional interference,and improve the utilization efficiency of signal energy.

Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract

The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission.To improve the limited communication distance and poor communication quality of the Internet of Vehicles(IoV),an optimal intelligent routing algorithm is proposed in this paper.Combined multiweight decision algorithm with the greedy perimeter stateless routing protocol,designed and evaluated standardized function for link stability.Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV.The blockchain system is used as the storage structure for relay data,and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission.The proposed scheme is simulated and analyzed under different scenarios and different parameters.The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.

Emerging Carbon Nanotube-Based Nanomaterials for Stable and Dendrite-Free Alkali Metal Anodes:Challenges,Strategies,and Perspectives

Alkali metals(Li,Na,and K)are promising candidates for high-performance rechargeable alkali metal battery anodes due to their high theoretical specific capacity and low electrochemical potential.However,the actual application of alkali metal anodes is impeded by the challenges of alkali metals,including their high chemical reactivity,uncontrolled dendrite growth,unstable solid electrolyte interphase,and infinite volume expansion during cycling processes.Introducing carbon nanotube-based nanomaterials in alkali metal anodesis an effective solution to these issues.These nanomaterials have attracted widespread attention owing to their unique properties,such as their high specific surface area,superior electronic conductivity,and excellent mechanical stability.Considering the rapidly growing research enthusiasm for this topic in the last several years,we review recent progress on the application of carbon nanotube-based nanomaterials in stable and dendrite-free alkali metal anodes.The merits and issues of alkali metal anodes,as well as their stabilizing strategies are summarized.Furthermore,the relationships among methods of synthesis,nano-or microstructures,and electrochemical properties of carbon nanotube-based alkali metal anodes are systematically discussed.In addition,advanced characterization technologies on the reaction mechanism of carbon nanotube-based nanomaterials in alkali metal anodes are also reviewed.Finally,the challenges and prospects for future study and applications of carbon nanotube-based AMAs in high-performance alkali metal batteries are discussed.

Immobilization of Oxyanions on the Reconstructed Heterostructure Evolved from a Bimetallic Oxysulfide for the Promotion of Oxygen Evolution Reaction

Efficient and durable oxygen evolution reaction(OER)requires the electrocatalyst to bear abundant active sites,optimized electronic structure as well as robust component and mechanical stability.Herein,a bimetallic lanthanum-nickel oxysulfide with rich oxygen vacancies based on the La_(2)O_(2)S prototype is fabricated as a binder-free precatalyst for alkaline OER.The combination of advanced in situ and ex situ characterizations with theoretical calculation uncovers the synergistic effect among La,Ni,O,and S species during OER,which assures the adsorption and stabilization of the oxyanion SO_(4)^(2-)onto the surface of the deeply reconstructed porous heterostructure composed of confining Ni OOH nanodomains by La(OH)_3 barrier.Such coupling,confinement,porosity and immobilization enable notable improvement in active site accessibility,phase stability,mass diffusion capability and the intrinsic Gibbs free energy of oxygen-containing intermediates.The optimized electrocatalyst delivers exceptional alkaline OER activity and durability,outperforming most of the Ni-based benchmark OER electrocatalysts.

Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles(IoV).Routing transmission solved the limitation of transmission distance to a certain extent.Traditional routing algorithm cannot adapt to complex traffic environment,resulting in low transmission efficiency.In order to improve the transmission success rate and quality of vehicle network routing transmission,make the routing algorithm more suitable for complex traffic environment,and reduce transmission power consumption to improve energy efficiency,a comprehensive optimized routing transmission algorithm is proposed.Based on the routing transmission algorithm,an optimization algorithmbased on road condition,vehicle status and network performance is proposed to improve the success rate of routing transmission in the IoV.Relative distance difference and density are used as decision-making indicators to measure Road Side Unit(RSU)assisted transmission.And the Ambient backscatter communication(AmBC)technology and energy collection are used to reduce the energy consumption of routing relay transmission.An energy collection optimization algorithm is proposed to optimize the energy efficiency of AmBC and improve the energy efficiency of transmission.Simulation results show that the proposed routing optimization algorithm can effectively improve the success rate of packet transmission in vehicular ad hoc networks(VANETs),and theAmBC optimization algorithmcan effectively reduce energy consumption in the transmission process.The proposed optimization algorithm achieves comprehensive optimization of routing transmission performance and energy efficiency.

Efficient hydrogen peroxide production enabled by exploring layered metal telluride as two electron oxygen reduction reaction electrocatalyst

It is of great interest to develop the novel transition metal-based electrocatalysts with high selectivity and activity for two electron oxygen reduction reaction(2e^(-) ORR).Herein,the nickel ditelluride(NiTe_(2)) with layered structure was explored as the 2e^(-) ORR electrocatalyst,which not only showed the highest 2e^(-) selectivity more than 97%,but also delivered a slight activity decay after 5000 cycles in alkaline media.Moreover,when NiTe_(2) was assembled as the electrocatalyst in H-type electrolyzer,the on-site yield of H_(2)O_(2) could reach up to 672 mmol h^(-1)g^(-1) under 0.45 V vs.RHE.Further in situ Raman spectra,theoretical calculation and post microstructural analysis synergistically unveiled that such a good 2e^(-) ORR performance could be credited to the intrinsic layered crystal structure,the high compositional stability,as well as the electron modulation on the active site Ni atoms by neighboring Te atoms,leading to the exposure of active sites as well as the optimized adsorption free energy of Ni to –OOH.More inspiringly,such telluride electrocatalyst has also been demonstrated to exhibit high activity and selectivity towards 2e^(-) ORR in neutral media.

Design and experiment of a picking robot for Agaricus bisporus based on machine vision

Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms.An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus,reduce harvesting costs,and improve harvesting efficiency.There are many disadvantages in manual picking,such as high labor intensity,time-consuming work and high cost.In this study,a set of mushroom picking platform including climbing mechanism,picking robot,and control system was designed and developed.The picking robot consisted of a truss mechanism,an image acquisition device,a mushroom collection device,and a picking actuator.The profile picking actuator could realize the function of constant force clamping.An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed.The algorithm included removal of abnormal noise points,background segmentation,coordinate conversion,and diameter detection.The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed,and the visual control interface was developed based on Labview.Through the performance test,the reliability of machine vision recognition and the overall operating stability of the picking platform were verified.The test results showed that in the process of machine vision recognition,the recognition accuracy rate was higher than 92.50%,the missed detection rate was lower than 4.95%,the false detection rate was lower than 2.15%,and the diameter measurement error was less than 4.50%.The image processing algorithm had high recognition rate and small diameter measurement error,which could meet the requirements of picking operation.The picking platform’s picking success rate was higher than 95.45%,the picking damage rate was lower than 3.57%,and the picking output rate was higher than 87.09%.Compared with manual picking,the recognition accuracy rate of the picking platform was increased by 6.70%,the picking output rate was increased by 1.51%.The overall performance of the picking platform was stable and practical.

Urea-induced interfacial engineering enabling highly reversible aqueous zinc-ion battery

Aqueous zinc-ion batteries(AZIBs)have been regarded as prospective rechargeable energy storage devices because of the high theoretical capacity and low redox potential of Zn metal.However,the uncontrollable formation of dendrites and the water-induced side reactions at the Zn/electrolyte interface,and the poor reversibility under a high current density(>2 mA·cm^(-2))and large area capacity(>2 mAh·cm^(-2))still limit the practical applications of AZIBs.Therefore,a strategy that can overcome these difficulties is urgently needed.Here,we introduce an environmentally friendly and low-cost additive,namely urea,to the electrolyte of AZIBs to induce uniform Zn deposition and suppress the side reactions.Measurements of the adsorption behavior,electrochemical characterization,and observations of the morphology revealed the interfacial modification induced by urea on the Zn/electrolyte interface,demonstrating its huge potential in AZIBs.Consequently,the long-term cycling stability(over2100 h)of a Zn/Zn symmetric cell under a high current density of 5 mA·cm^(-2)and a capacity of 5 mAh·cm^(-2)was achieved with a 1 mol·L^(-1)ZnSO_(4)electrolyte with the urea additive.Additionally,the assembled Zn/NH_4V_4O_(10)full cell with urea exhibited excellent cycling performance and an outstanding average Coulombic efficiency of 99.98%.These results indicate that this is a low-cost and effective additive strategy for realizing highly reversible AZIBs.

Online diagnosis platform for tomato seedling diseases in greenhouse production

The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.

Non-invasive Healthcare Analytical Platform Based on Organic Electrochemical Transistors

Non-invasive bioelectronics,especially organic electrochemical transistors(OECTs),have drawn extensive attentions of academical and medical communities by virtue of their efficient bio-electronic interfacing,water-involved ionic transport,excellent ionic-electronic coupling,ultralow power consumption,wide detectable range,and outstanding detection sensitivity.Designable structure diversity,low-temperature solution processability,facile bio/chemical functionalization,and excellent biocompatibility of organic mixed ionic-electronic conductors(OMIECs)render OECTs particularly suitable for non-invasive or minimally invasive healthcare analytical platform.Here,we comprehensively review recent advances of the non-invasive analytical healthcare applications based on OECTs,especially on the detection of biomarkers or metabolites in the excretory biofluids,as well as the recording of electrophysiological signals.A brief introduction of OECT and its comparison with other organic thin-film transistors upon device configuration and working mechanism are firstly discussed.State-of-the-art non-invasive OECT-based biosensors are summarized on their detection of ionic and molecular biomarkers,following with circuit design strategies of OECTs for real-time and in-situ electrophysiological recording from skin surface.In conclusion,remaining barriers and future challenges of non-invasive OECT-based bioelectronics towards lower detection limit,more accurate quantitative relationship between analyte concentrations and measured parameters,more intimate device-tissue interface,and long-term operation stability are deeply analyzed with a critical outlook.

Method for the fruit tree recognition and navigation in complex environment of an agricultural robot

To realize the visual navigation of agricultural robots in the complex environment of orchards,this study proposed a method for fruit tree recognition and navigation based on YOLOv5.The YOLOv5s model was selected and trained to identify the trunks of the left and right rows of fruit trees;the quadratic curve was fitted to the bottom center of the fruit tree recognition box,and the identified fruit trees were divided into left and right columns by using the extreme value point of the quadratic curve to obtain the left and right rows of fruit trees;the straight-line equation of the left and right fruit tree rows was further solved,the median line of the two straight lines was taken as the expected navigation path of the robot,and the path tracing navigation experiment was carried out by using the improved LQR control algorithm.The experimental results show that under the guidance of the machine vision system and guided by the improved LQR control algorithm,the lateral error and heading error can converge quickly to the desired navigation path in the four initial states of[0 m,−0.34 rad],[0.10 m,0.34 rad],[0.15 m,0 rad]and[0.20 m,−0.34 rad].When the initial speed was 0.5 m/s,the average lateral error was 0.059 m and the average heading error was 0.2787 rad for the navigation trials in the four different initial states.Its average driving was 5.3 m into the steady state,the average value of steady state lateral error was 0.0102 m,the average value of steady state heading error was 0.0253 rad,and the average relative error of the robot driving along the desired navigation path was 4.6%.The results indicate that the navigation algorithm proposed in this study has good robustness,meets the operational requirements of robot autonomous navigation in orchard environment,and improves the reliability of robot driving in orchard.

Detection of the farmland plow areas using RGB-D images with an improved YOLOv5 model

Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was analyzed.This method applied advanced computer vision technology to the field of agricultural automation.Adopting and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow areas.The GhostConv module was also utilized to reduce parameter and computational complexity.Second,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed area.Last,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance measurements.The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection distance.The ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational requirements.The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.

Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

Spatially resolved transcriptomics(SRT)is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition.To fully understand the organizational complexity and tumor immune escape mechanism,we propose stMGATF,a multiview graph attention fusion model that integrates gene expression,histological images,spatial location,and gene association.To better extract information,stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view.A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation.Applied to diverse SRT datasets,stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms.In particular,stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains.Importantly,considering the associations between genes in tumors,stMGATF can identify the spatial dark genes ignored by traditional methods,which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms,providing theoretical evidence for the development of new immunotherapeutic strategies.

Improved material descriptors for bulk modulus in intermetallic compounds via machine learning

Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic compounds.In this study,bulk modulus datasets of intermetallic compounds were collected,and the features affecting the bulk modulus of intermetallics were screened via feature engineering.Three features B_(cal),dB_(avg),and TIE(corresponding to calculated bulk modulus,mean bulk modulus,and third ionization energy,respectively)were found to be the dominant factors influencing bulk modulus and can be extended to other multi-component alloys.Particularly,we predicted the bulk modulus with an accuracy of 95%using surrogate machine learning models with the selected features,and these features were also demonstrated to be effective for high-entropy alloys.Moreover,symbolic regression provided an expression for the relationship between bulk modulus and the screened features.The machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds.

Design and experiment of conical diversion virus-free potato minituber precision seed-metering device

In order to accelerate the development of sowing mechanization of virus-free potato minituber,a conical diversion virus-free potato minituber precision seed-metering Device was designed according to the structural characteristics and agronomic requirements of virus-free potato minituber.The device is mainly composed of conical turntable,transmission shaft,outer baffle of seed-metering Device,baffle,seed outlet,seed cleaning device,type hole,etc.The working principle of conical diversion precision seed-metering device for virus-free potato minituber was expounded,and the stress analysis of virus-free potato minituber in each region was carried out.By EDEM discrete element simulation software,the structure of the type hole is optimized to determine the optimal structure of the type hole and according to the physical characteristics of virus-free potato minituber,the single factor experiments of the effects of length of type hole,cone disc speed and cone disc angle on seed filling performance were completed.The orthogonal regression test was carried out with the length of type hole,cone disc speed,and cone disc angle as the test objects,and the leakage rate and qualified rate as the response indexes.The regression models of leakage rate,replay rate,and qualified rate were established,and the parameters of the regression model were optimized.The optimal parameter combination is that the length of type hole is 33.61 mm,the cone disc speed is 6.35 r/min,and the cone disc angle is 26.59°.Bench test was carried out under the optimal conditions,the leakage rate was 3.80%,the replay rate was 0.80%,and the qualified rate was 95.40%,which was basically consistent with the prediction results of the regression model,and met the requirements of precision sowing of virus-free potato minituber.

Development of the magnetic separation device for surface cracks of delinted cottonseed

In seed processing for commercial production,the collision or friction between mechanical parts and seed is inevitable.These collisions or friction can cause cracks on the surface of the seed,which can affect germination rates and ultimately reduce crop yields.Using the difference of the seed motion trajectories with different crack degrees in the magnetic field,the grading of the surface crack seed can be realized.In this study,the motion law of seed in a non-uniform magnetic field was analyzed by taking the delinted cottonseed as an object.A magnetic separation device for crop seeds was developed.This device was essentially composed of the feed throat,magnetic roller,conveyor belt,and variable-frequency adjustable-speed motor.The magnetic powder adhering seeds enter the magnetic separation device through the feed throat and are conveyed to the magnetic roller through the conveyor belt.The magnetic roller has different adsorption forces on seeds with different degrees of cracking,and seeds fall into different outlets,thus completing the grading of the seeds.By adjusting working parameters such as the magnetic field strength and the speed of the conveyor belt,magnetic separation could be made.In order to verify the grading effect,the magnetic separation accuracy was taken as the inspection index,the magnetic powder’s mesh number,the mass mixing ratio between magnetic powder and delinted cottonseed,and the rotation speed of the magnetic roller were taken as the inspection factors.The response surface methodology was used to optimize the working parameters of the experimental device.The results showed that the optimal process parameters of the magnetic separation device were as follows:the number of magnetic powder meshes was 250,the mass mixing ratio between magnetic powder and delinted cottonseed was 1:20,and when the rotational speed of the magnetic separation roller was 20 r/min,the detection rate reached 92.5%.The designed magnetic separation device can realize the non-destructive batch detection of seed surface cracks with high work efficiency and has guiding significance for the high-precision and low-damage classification detection and classification of commercial seed.

Enhanced magnetic properties of SmCo/FeCo nanocomposite magnets by doping eutectic Sm-Ni alloy

In this work,we prepared SmCo/FeCo nanocomposite magnets with an enhanced magnetic performance by doping eutectic Sm-Ni alloy.The magnets without Sm-Ni alloy,with a grain size of 5-12 nm and an average size of~ 9 nm,are composed of Sm_(2)Co_(17) phase and FeCo phase,leading to a relatively low remanence of 0.0365A·m^(2)·g^(-1) and coercivity of 0.15 T,respectively.After doping 2.5 wt% Sm-Ni alloy,there is no obvious change on the grain size of SmCo/FeCo magnets.