Effects of vacancy and external electric field on the electronic properties of the MoSi_(2)N_(4)/graphene heterostructure

Recently,the newly synthesized septuple-atomic layer two-dimensional(2D)material MoSi_(2)N_(4)(MSN)has attracted attention worldwide.Our work delves into the effect of vacancies and external electric fields on the electronic properties of the MSN/graphene(Gr)heterostructure using first-principles calculation.We find that four types of defective structures,N-in,N-out,Si and Mo vacancy defects of monolayer MSN and MSN/Gr heterostructure are stable in air.Moreover,vacancy defects can effectively modulate the charge transfer at the interface of the MSN/Gr heterostructure as well as the work function of the pristine monolayer MSN and MSN/Gr heterostructure.Finally,the application of an external electric field enables the dynamic switching between n-type and p-type Schottky contacts.Our work may offer the possibility of exceeding the capabilities of conventional Schottky diodes based on MSN/Gr heterostructures.

Study on neutron-gamma discrimination methods based on GMM-KNN and LabVIEW implementation

Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mixture model(GMM)with the K-nearest neighbor(KNN)algorithm,referred to as GMM-KNN.First,the unlabeled training and test data were categorized into three energy ranges:0–25 keV,25–100 keV,and 100–2100 keV.Second,GMM-KNN achieved small-batch clustering in three energy intervals with only the tail integral Q_(tail) and total integral Q_(total) as the pulse features.Subsequently,we selected the pulses with a probability greater than 99%from the GMM clustering results to construct the training set.Finally,we improved the KNN algorithm such that GMM-KNN realized the classification and regression algorithms through the LabVIEW language.The outputs of GMM-KNN were the category or regression predictions.The proposed GMM-KNN constructed the training set using unlabeled real pulse data and realized n-γdiscrimination of ^(241)Am-Be pulses using the LabVIEW program.The experimental results demonstrated the high robustness and flexibility of GMM-KNN.Even when using only 1/4 of the training set,the execution time of GMM-KNN was only 2021 ms,with a difference of only 0.13%compared with the results obtained on the full training set.Furthermore,GMM-KNN outperformed the charge comparison method in terms of accuracy,and correctly classified 5.52%of the ambiguous pulses.In addition,the GMM-KNN regressor achieved a higher figure of merit(FOM),with FOM values of 0.877,1.262,and 1.020,corresponding to the three energy ranges,with a 32.08%improvement in 0–25 keV.In conclusion,the GMM-KNN algorithm demonstrates accurate and readily deployable real-time n-γdiscrimination performance,rendering it suitable for on-site analysis.

First‑principles study on electronic structure,optical and magnetic properties of rare earth elements X(X=Sc,Y,La,Ce,Eu)doped with two‑dimensional GaSe

The electronic structure,magnetic,and optical properties of two-dimensional(2D)GaSe doped with rare earth elements X(X=Sc,Y,La,Ce,Eu)were calculated using the first-principles plane wave method based on den-sity functional theory.The results show that intrinsic 2D GaSe is a p-type nonmagnetic semiconductor with an indi-rect bandgap of 2.6611 eV.The spin-up and spin-down channels of Sc-,Y-,and La-doped 2D GaSe are symmetric,they are non-magnetic semiconductors.The magnetic moments of Ce-and Eu-doped 2D GaSe are 0.908μ_(B)and 7.163μ_(B),which are magnetic semiconductors.Impurity energy levels appear in both spin-up and spin-down chan-nels of Eu-doped 2D GaSe,which enhances the probability of electron transition.Compared with intrinsic 2D GaSe,the static dielectric constant of the doped 2D GaSe increases,and the polarization ability is strengthened.The ab-sorption spectrum of the doped 2D GaSe shifts in the low-energy direction,and the red-shift phenomenon occurs,which extends the absorption spectral range.The optical reflection coefficient of the doped 2D GaSe is improved in the low energy region,and the improvement of Eu-doped 2D GaSe is the most obvious.

Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research topics.Currently,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering system.Particularly,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods.In this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization.Experiments on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 respectively.On FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within limits.Thus,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

FIR-YOLACT:Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.

Magnetic activity and orbital parameters of CC Com based on photometric data,LAMOST low-and medium-resolution spectra

In this paper,we present four sets of photometric VRI light curves,and several LAMOST low and medium resolution spectra of contact binary CC Com.We revised the orbital parameters by simultaneously combining with previously published radial velocity measurements using the WilsonDevinney program.We used light curves at different observational times to obtain the starspot parameters.The values of the starspot radius are variable in short-and long-term scales,and their longitudes are stable.We updated the orbital period change of CC Com,and analyzed the periodic variation.The period of CC Com decreases at a rate of 4.66(±0.20)×10^(-11)d yr^(-1),which may be due to mass transfer from the secondary component to the primary component.The oscillation of its orbital period with a period of17.18(0.08)years and amplitude of 0.0018(1)d may be caused by the light time effect(LITE)via a third body of 0.06 M☉dwarf or magnetic activity cycle.Furthermore,we obtained one optical spectrum from the LAMOST survey,which gives the spectral type of CC Com as K7±2 V.Strong emissions exist in the Ha,and Ca 11 H&K lines in the observed spectrum,indicating strong chromospheric activity on CC Com.In the 12 LAMOST medium-resolution spectra,the EWs of Ha line are variable along the phase and time,which may be a plage or flare event.

Rucklidge-based memristive chaotic system:Dynamic analysis and image encryption

A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is performed.The sensitivity of the system to parameters allows it obtains 16 different attractors by changing only one parameter.The various transient behaviors and excellent spectral entropy and C0 complexity values of the system can also reflect the high complexity of the system.A circuit is designed and verified the feasibility of the system from the physical level.Finally,the system is applied to image encryption,and the security of the encryption system is analyzed from multiple aspects,providing a reference for the application of such memristive chaotic systems.

Graphene effectively activating "dead" water molecules between manganese dioxide layers in potassium-ion battery

Aqueous potassium-ion batteries(APIBs),recognized as safe and reliable new energy devices,are considered as one of the alternatives to traditional batteries.Layered MnO_(2),serving as the main cathode,exhibits a lower specific capacity in aqueous electrolytes compared to organic systems and operates through a different reaction mechanism.The application of highly conductive graphene may effectively enhance the capacity of APIBs but could complicate the potassium storage environment.In this study,a MnO_(2) cathode pre-intercalated with K~+ions and grown on graphene(KMO@rGO) was developed using the microwave hydrothermal method for APIBs.KMO@rGO achieved a specific capacity of 90 mA h g^(-1) at a current density of 0.1 A g^(-1),maintaining a capacity retention rate of>90% after 5000 cycles at 5 A g^(-1).In-situ and exsitu characterization techniques revealed the energy-storage mechanism of KMO@rGO:layered MnO_(2)traps a large amount of "dead" water molecules during K~+ions removal.However,the introduction of graphene enables these water molecules to escape during K~+ ions insertion at the cathode.The galvanostatic intermittent titration technique and density functional theory confirmed that KMO@rGO has a higher K~+ions migration rate than MnO_(2).Therefore,the capacity of this cathode depends on the interaction between dead water and K~+ions during the energy-storage reaction.The optimal structural alignment between layered MnO_(2) and graphene allows electrons to easily flow into the external circuit.Rapid charge compensation forces numerous low-solvent K~+ions to displace interlayer dead water,enhancing the capacity.This unique reaction mechanism is unprecedented in other aqueous battery studies.

APSO-CNN-SE:An Adaptive Convolutional Neural Network Approach for IoT Intrusion Detection

The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed Denial of Service(DDoS)attacks triggered by botnets,have resulted in information leakage and property damage.Therefore,developing an efficient and realistic intrusion detection system(IDS)is critical for ensuring IoT network security.In recent years,traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic,and the excellent performance of deep learning techniques,as an advanced version of machine learning,has led to their widespread application in intrusion detection.In this paper,we propose an Adaptive Particle Swarm Optimization Convolutional Neural Network Squeeze-andExcitation(APSO-CNN-SE)model for implementing IoT network intrusion detection.A 2D CNN backbone is initially constructed to extract spatial features from network traffic.Subsequently,a squeeze-and-excitation channel attention mechanism is introduced and embedded into the CNN to focus on critical feature channels.Lastly,the weights and biases in the CNN-SE are extracted to initialize the population individuals of the APSO.As the number of iterations increases,the population’s position vector is continuously updated,and the cross-entropy loss function value is minimized to produce the ideal network architecture.We evaluated the models experimentally using binary and multiclassification on the UNSW-NB15 and NSL-KDD datasets,comparing and analyzing the evaluation metrics derived from each model.Compared to the base CNN model,the results demonstrate that APSO-CNNSE enhances the binary classification detection accuracy by 1.84%and 3.53%and the multiclassification detection accuracy by 1.56%and 2.73%on the two datasets,respectively.Additionally,the model outperforms the existing models like DT,KNN,LR,SVM,LSTM,etc.,in terms of accuracy and fitting performance.This means that the model can identify potential attacks or anomalies more precisely,improving the overall security and stability of the IoT environment.

Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.

Structural, mechanical, and electronic properties of 25 kinds of Ⅲ–Ⅴbinary monolayers: A computational study with first-principles calculation

Using first-principle calculations, we investigate the mechanical, structural, and electronic properties and formation energy of 25 kinds of Ⅲ–V binary monolayers in detail. A relative radius of the binary compound according to the atomic number in the periodic table is defined, and based on the definition, the 25 kinds of Ⅲ–V binary compounds are exactly located at a symmetric position in a symmetric matrix. The mechanical properties and band gaps are found to be very dependent on relative radius, while the effective mass of holes and electrons are found to be less dependent. A linear function between Young’s modulus and formation energy is fitted with a linear relation in this paper. The change regularity of physical properties of B–V(V = P, As, Sb, Bi) and Ⅲ–N(Ⅲ = Al, Ga, In, Tl) are found to be very different from those of other Ⅲ–V binary compounds.

Geant4 simulation of multi-sphere spectrometer response function and the detection of 241Am–Be neutron spectrum

This paper is aimed at detecting the neutron spectrum of^(241)Am–Be, a widely used neutron source, with the SP9 ~3He proportional counter, which is a multi-sphere spectrometer system of eight thermal neutron detectors embedded in eight polyethylene(PE) spheres of varying diameters. The transport processes of a neutron in the multi-sphere spectrometer are simulated using the Geant4 code. Two sets of response functions of the PE spheres are obtained for calculating the^(241)Am–Be neutron spectrum.Response Function 1 utilizes the thermal neutron scattering model G4 Neutron HPThermal Scattering for neutron energies of ≤4 eV, and Response Function 2 has no thermal treatment. Neutron spectra of an^(241)Am–Be neutron source are measured and compared to those calculated by using the response functions. The results show that response function with thermal treatment is more accurate and closer to the real spectrum.

Effect of initial crystallization temperature and surface diffusion on formation of GaAs multiple concentric nanoring structures by droplet epitaxy

GaAs multiple concentric nano-ring structures(CNRs)are prepared with multistep crystallization procedures by droplets epitaxy on GaAs(001)to explore the influence of different initial crystallization temperatures on CNRs morphology.Atomic force microscope(AFM)images show that GaAs nanostructures are more likely to form elliptical rings due to diffusion anisotropy.Meanwhile,with the increase of initial crystallization temperature,the inner ring height and density of CNRs are increased,and outer rings are harder to form.In addition,the mechanism of formation of CNRs is discussed by classical nucleation theory and diffusion theory.The method can be used to calculate the diffusion activation energy of gallium atoms(0.7±0.1 eV)on the GaAs(001)surface conveniently.

Modulation of Schottky barrier in XSi_(2)N_(4)/graphene(X=Mo and W)heterojunctions by biaxial strain

Reducing the Schottky barrier height(SBH)and even achieving the transition from Schottky contacts to Ohmic contacts are key challenges of achieving high energy efficiency and high-performance power devices.In this paper,the modulation effects of biaxial strain on the electronic properties and Schottky barrier of Mo Si_(2)N_(4)(MSN)/graphene and WSi_(2)N_(4)(WSN)/graphene heterojunctions are examined by using first principles calculations.After the construction of heterojunctions,the electronic structures of MSN,WSN,and graphene are well preserved.Herein,we show that by applying suitable external strain to a heterojunction stacked by MSN or WSN—an emerging two-dimensional(2D)semiconductor family with excellent mechanical properties—and graphene,the heterojunction can be transformed from Schottky ptype contacts into n-type contacts,even highly efficient Ohmic contacts,making it of critical importance to unleash the tremendous potentials of graphene-based van der Waals(vd W)heterojunctions.Not only are these findings invaluable for designing high-performance graphene-based electronic devices,but also they provide an effective route to realizing dynamic switching either between n-type and p-type Schottky contacts,or between Schottky contacts and Ohmic contacts.

Microstructure,optical,and photoluminescence properties ofβ-Ga_(2)O_(3)films prepared by pulsed laser deposition under different oxygen partial pressures

Theβ-Ga_(2)O_(3)films are prepared on polished Al_(2)O_(3)(0001)substrates by pulsed laser deposition at different oxygen partial pressures.The influence of oxygen partial pressure on crystal structure,surface morphology,thickness,optical properties,and photoluminescence properties are studied by x-ray diffraction(XRD),atomic force microscope(AFM),scanning electron microscope(SEM),spectrophotometer,and spectrofluorometer.The results of x-ray diffraction and atomic force microscope indicate that with the decrease of oxygen pressure,the full width at half maximum(FWHM)and grain size increase.With the increase of oxygen pressure,the thickness of the films first increases and then decreases.The room-temperature UV-visible(UV-Vis)absorption spectra show that the bandgap of theβ-Ga_(2)O_(3)film increases from4.76 e V to 4.91 e V as oxygen pressure decreasing.Room temperature photoluminescence spectra reveal that the emission band can be divided into four Gaussian bands centered at about 310 nm(~4.0 e V),360 nm(~3.44 e V),445 nm(~2.79 e V),and 467 nm(~2.66 e V),respectively.In addition,the total photoluminescence intensity decreases with oxygen pressure increasing,and it is found that the two UV bands are related to self-trapped holes(STHs)at O1 sites and between two O2-s sites,respectively,and the two blue bands originate from V_(Ga)^(2-)at Ga1 tetrahedral sites.The photoluminescence mechanism of the films is also discussed.These results will lay a foundation for investigating the Ga_(2)O_(3)film-based electronic devices.

The Effect on the Electric Structure and Optical Properties of Ca2Ge Bulk with Sr-Doping

The electronic structure and the optical properties of Ca2Ge have been calculated by the first-principles pseudo potential method. The results of the electric structure show that Ca2Ge bulk is a direct semiconductor with the band gap of 0.306 eV, the conduction band is mainly composed of Ca 3d, the valence bands is mainly composed of Ge 3p. With Sr-doping, Ca2Ge bulk is a direct semiconductor with the band gap of 0.350 eV, the conduction bands are mainly composed of Ca 3d and Sr 3d, the valence bands are mainly composed of Ge 3p and Sr 3d. The results of the optical properties show that the dielectric constant of Ca2Ge bulk is reduced from 21.52 to 13.94, the reflectivity is decreased, and the absorption is increased with Sr-doping. The optical properties are improved with Sr-doping, the results offer theoretical guide for the optical properties control of Ca2Ge.

Evolution of defects and deformation mechanisms in different tensile directions of solidified lamellar Ti-Al alloy

Two-phaseγ-TiAl/α_(2)-Ti_(3)Al lamellar intermetallics have attracted considerable attention because of their excellent strength and plasticity.However,the exact deformation mechanisms remain to be investigated.In this paper,a solidified lamellar Ti-Al alloy with lamellar orientation at 0°,17°,and 73°with respect to the loading direction was stretched by utilizing molecular dynamics(MD)simulations.The results show that the mechanical properties of the sample are considerably influenced by solidified defects and tensile directions.The structure deformation and fracture were primarily attributed to an intrinsic stacking fault(ISF)accompanied by the nucleated Shockley dislocation,and the adjacent extrinsic stacking fault(ESF)and ISF formed by solidification tend to form large HCP structures during the tensile process loading at 73°.Moreover,cleavage cracking easily occurs on theγ/α_(2)interface under tensile deformation.The fracture loading mechanism at 17°is grain boundary slide whereas,at 73°and 0°,the dislocation piles up to form a dislocation junction.

Band structure of silicon and germanium thin films based on first principles

In nanomaterials, optical anisotropies reveal a fundamental relationship between structural and optical properties, in which directional optical properties can be exploited to enhance the performance of optoelectronic devices. First principles calculation based on density functional theory （DFT） with the generalized gradient approximation （GGA） are carried out to investigate the energy band gap structure on silicon （Si） and germanium （Ge） nanofilms. Simulation results show that the band gaps in Si （100） and Ge （111） nanofilms become the direct-gap structure in the thickness range less than 7.64 nm and 7.25 nm respectively, but the band gaps of Si （111） and Ge （110） nanofilms still keep in an indirect-gap structure and are independent on film thickness, and the band gaps of Si （110） and Ge （100） nanofilms could be transferred into the direct-gap structure in nanofilms with smaller thickness. It is amazing that the band gaps of Si（1-x）/ZGexSi（1-x）/2 sandwich structure become the direct-gap structure in a certain area whether （111） or （100） surface. The band structure change of Si and Ge thin films in three orientations is not the same and the physical mechanism is very interesting, where the changes of the band gaps on the Si and Ge nanofilms follow the quantum confinement effects.

Luminescence properties and optical sensing behaviors of Sr_(2)GdSbO_(6):Eu^(3+) phosphors

Novel orange-red Sr_(2)GdSbO_(6):xEu^(3+)(x=0,0.05,0.1,0.2,0.3,0.4,0.5 and 0.6) phospho rs were successfully prepared by the traditional high-temperature solid-state method.The results of Rietveld refinement,energy dispersive spectroscopy(EDS) spectrum and elemental mapping demonstrate that Eu^(3+) successfully replaces the Gd^(3+) sites and distributes uniformly in the particles of phosphors.The luminescence properties of Sr_(2)GdSbO_(6):Eu_(3+)phosphors were investigated in detail.The emission spectra of the strongest emission peak is the ^(5)D_(0)→^(7)F_(1)(593 nm) transition,which can emit orange-red light under393 nm excitation.When the doping concentration of Eu3+ions is x=0.2,the luminescence intensity of the phosphors reaches the highest.The detailed mechanism of concentration quenching is attributed to dipole-dipole interaction.The thermal stability values of Sr_(2)GdSbO_(6):0.2Eu^(3+) phosphors are 87%,82% and114% under 393,467 and 527 nm excitations,respectively.The causes of the abnormal thermal quenching under 527 nm excitation were analyzed.Based on the abnormal thermal quenching under527 nm excitation,the optical thermometry properties of Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors were investigated by fluorescence intensity ratio(FIR) technique,and appreciable relative sensitivity was obtained.The results suggest that Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors can be potentially applied to w-LEDs and optical thermometers.

Dislocation mechanism of Ni_(47)Co_(53) alloy during rapid solidification

Dislocations and other atomic-level defects play a crucial role in determining the macroscopic properties of crystalline materials,but it is extremely difficult to observe the evolution of dislocations due to the limitations of the most advanced experimental techniques.Therefore,in this work,the rapid solidification processes of Ni_(47)Co_(53) alloy at five cooling rates are studied by molecular dynamics simulation,and the evolutions of their microstructures and dislocations are investigated as well.The results show that face-centered cubic(FCC) structures are formed at the low cooling rate,and the crystalline and amorphous mixture appear at the critical cooling rate,and the amorphous are generated at the high cooling rate.The crystallization temperature and crystallinity decrease with cooling rate increasing.Dislocations are few at the cooling rates of 1×10^(11) K/s,5×10^(12) K/s,and 1×10^(13) K/s,and they are most abundant at the cooling rates of 5×10^(11) K/s and1 × 10^(12) K/s,in which their dislocation line lengths are both almost identical.There appear a large number of dislocation reactions at both cooling rates,in which the interconversion between perfect and partial dislocations is primary.The dislocation reactions are more intense at the cooling rate of 5×10^(11) K/s,and the slip of some dislocations leads to the interconversion between FCC structure and hexagonal close packed(HCP) structure,which causes the twin boundaries(TBs) to disappear.The FCC and HCP are in the same atomic layer,and dislocations are formed at the junction due to the existence of TBs at the cooling rate of 1 ×10^(12) K/s.The present research is important in understanding the dislocation mechanism and its influence on crystal structure at atomic scales.