Enhancing potassium-ion storage of Bi_(2)S_(3) through external–internal dual synergism: Ti_(3)C_(2)T_(x) compositing and Cu^(2+) doping

Potassium-ion batteries(PIBs)offer a cost-effective and resource-abundant solution for large-scale energy storage.However,the progress of PIBs is impeded by the lack of high-capacity,long-life,and fast-kinetics anode electrode materials.Here,we propose a dual synergic optimization strategy to enhance the K^(+)storage stability and reaction kinetics of Bi_(2)S_(3) through two-dimensional compositing and cation doping.Externally,Bi_(2)S_(3) nanoparticles are loaded onto the surface of three-dimensional interconnected Ti_(3)C_(2)T_(x) nanosheets to stabilize the electrode structure.Internally,Cu^(2+)doping acts as active sites to accelerate K^(+)storage kinetics.Various theoretical simulations and ex situ techniques are used to elucidate the external–internal dual synergism.During discharge,Ti_(3)C_(2)T_(x) and Cu^(2+)collaboratively facilitate K+intercalation.Subsequently,Cu^(2+)doping primarily promotes the fracture of Bi2S3 bonds,facilitating a conversion reaction.Throughout cycling,the Ti_(3)C_(2)T_(x) composite structure and Cu^(2+)doping sustain functionality.The resulting Cu^(2+)-doped Bi2S3 anchored on Ti_(3)C_(2)T_(x)(C-BT)shows excellent rate capability(600 mAh g^(-1) at 0.1 A g^(–1);105 mAh g^(-1) at 5.0 A g^(-1))and cycling performance(91 mAh g^(-1) at 5.0 A g^(-1) after 1000 cycles)in half cells and a high energy density(179 Wh kg–1)in full cells.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

Creation and annihilation of artificial magnetic skyrmions with the electric field

Recent theory and experiments show that artificial magnetic skyrmions can be stabilized at room temperature without the need for the external magnetic field,casting strong potentials for the device applications.In this work,we study the electric field manipulation of artificial magnetic skyrmions imprinted by Co disks on CoPt multilayers utilizing the micromagnetic simulations.We find that the reversible annihilation and creation of skyrmions can be realized with the electric field via the strain mediated magnetoelastic coupling.In addition,we also demonstrate controllable manipulation of individual skyrmion,which opens a new platform for constructing magnetic field-free and low-energy dissipation skyrmion based media.

Synergistic effect of total ionizing dose on single-event gate rupture in SiC power MOSFETs

The synergistic effect of total ionizing dose(TID) and single event gate rupture(SEGR) in SiC power metal–oxide–semiconductor field effect transistors(MOSFETs) is investigated via simulation. The device is found to be more sensitive to SEGR with TID increasing, especially at higher temperature. The microscopic mechanism is revealed to be the increased trapped charges induced by TID and subsequent enhancement of electric field intensity inside the oxide layer.

Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification

Dear editor,This letter presents a practical industrial process identification scheme.More specifically,to improve the identification accuracy of practical process,a decoupled identification scheme is developed based on neural fuzzy network and autoregressive exogenous(ARX)model,which is based on multi-signal sources.The multiple signal sources include binary signals and random signals.Experimental results of pH neutralization process show that developed identification scheme can provide accurate identification accuracy.

Systematics on production of superheavy nuclei Z=119-122 in fusion-evaporation reactions

The fusion dynamics of the formation of super-heavy nuclei were investigated thoroughly within the din-uclear system model.The Monte Carlo approach was implemented in the nucleon transfer process to include all possible orientations,at which the dinuclear system is assumed to be formed at the touching configuration of dinuclear fragments.The production cross sections of superheavy nuclei Cn,Fl,Lv,Ts,and Og were calculated and compared with the available data from Dubna.The evaporation residue excitation functions in the channels of pure neutrons and charged particles were systematically analyzed.The combinations of 44 Sc,48;50 Ti,49;51 V,52;54 Cr,58;62 Fe,and 62;64 Ni bombarding the actinide nuclides 238 U,244 Pu,248 Cm,247;249 Bk,249;251 Cf,252 Es,and 243 Am were calculated to produce the superheavy elements with Z¼119?122.We obtained that the production cross sections sensitively depend on the neutron richness of the reaction system.The structure of the evaporation residue excitation function is related to the neutron separation energy and fission barrier of the compound nucleus.

Adaptive control of track tension estimation using radial basis function neural network

Track tension is a major factor influencing the reliability of a track.In order to reduce the risk of track peel-off,it is necessary to keep track tension constant.However,it is difficult to measure the dynamic tension during off-road operation.Based on the analysis of the relation and external forces depending on free body diagrams of the idler,idler arm,road wheel and road arm,a theoretical estimation model of track tension is built.Comparing estimation results with multibody dynamics simulation results,the rationality of track tension monitor is validated.By the aid of this monitor,a track tension control system is designed,which includes a self-tuning proportional-integral-derivative(PID)controller based on radial basis function neural network,an electro-hydraulic servo system and an idler arm.The tightness of track can be adjusted by turning the idler arm.Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately.Compared with a traditional PID controller,the proposed controller has a stronger anti-disturbance ability by amending control parameters online.

A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades

Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.

Lattice Boltzmann model for interface capturing of multiphase flows based on Allen–Cahn equation

A phase-field-based lattice Boltzmann model is proposed for the interface capturing of multi-phase flows based on the conservative Allen–Cahn equation(ACE).By adopting the improved form of a relaxation matrix and an equilibrium distribution function,the time derivative?t(φ)induced by recovering the diffusion term in ACE is eliminated.The conducted Chapman–Enskog analysis demonstrates that the correct conservative ACE is recovered.Four benchmark cases including Zalesak’s disk rotation,vortex droplet,droplet impact on thin film,and Rayleigh–Taylor instability are investigated to validate the proposed model.The numerical results indicate that the proposed model can accurately describe the complex interface deformation.

Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches.The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network(NFN)and a linear state`-space model.The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals,which consist of step signals and random signals.First,based on the characteristic that step signals do not excite static nonlinear systems,that is,the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input,the unknown intermediate variables can be replaced by inputs,solving the problem of unmeasurable intermediate variable information.In the presence of step signals,the parameters of the state-space model are estimated using the recursive extended least squares(RELS)algorithm.Moreover,to effectively deal with the interference of measurement noises,a data filtering technique is introduced,and the filtering-based RELS is formulated for estimating the NFN by employing random signals.Finally,according to the structure of the Hammerstein system,the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system,and it can then be easily controlled by applying a linear controller.The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

A Computational Framework for Parachute Inflation Based on Immersed Boundary/Finite Element Approach

A computational framework for parachute inflation is developed based on the immersed boundary/finite element approach within the open-source IBAMR library.The fluid motion is solved by Peskin's diffuse-interface immersed boundary(IB)method,which is attractive for simulating moving-boundary flows with large deformations.The adaptive mesh refinement technique is employed to reduce the computational cost while retain the desired resolution.The dynamic response of the parachute is solved with the finite element approach.The canopy and cables of the parachute system are modeled with the hyperelastic material.A tether force is introduced to impose rigidity constraints for the parachute system.The accuracy and reliability of the present framework is validated by simulating inflation of a constrained square plate.Application of the present framework on several canonical cases further demonstrates its versatility for simulation of parachute inflation.

A nano-sheet graphene-based enhanced thermal radiation composite for passive heat dissipation from vehicle batteries

In response to thermal runaway(TR)of electric vehicles,recent attention has been focused on mitigation strategies such as efficient heat dredging in battery thermal management.Thermal management with particular focus on battery cooling has been becoming increasingly significant.TR usually happened when an electric vehicle is unpowered and charged.In this state,traditional active battery cooling schemes are disabled,which can easily lead to dangerous incidents due to loss of cooling ability,and advanced passive cooling strategies are therefore gaining importance.Herein,we developed an enhanced thermal radiation material,consisting of~1μm thick multilayered nano-sheet graphene film coated upon the heat dissipation surface,thereby enhancing thermal radiation in the nanoscale.The surface was characterized on the nanoscale,and tested in a battery-cooling scenario.We found that the graphene-based coating's spectral emissivity is between 91% and 95% in the mid-infrared region,and thermal experiments consequently illustrated that graphene-based radiative cooling yielded up to15.1% temperature reduction when compared to the uncoated analogue.Using the novel graphene surface to augment a heat pipe,the temperature reduction can be further enlarged to 25.6%.The new material may contribute to transportation safety,global warming mitigation and carbon neutralization.

Aerodynamic Characteristics and Noise Collaborative Optimization of an Airfoil

Aerodynamic noise is the main problem restricting its development nowadays in green energy,ocean engineering and aerospace engineering.In order to limit the aerodynamic noise of an airfoil structure,a method is proposed in this paper by designing low noise airfoils.This method optimized the aerodynamic noise of two-dimensional airfoil,and considered the aerodynamic performance of the airfoil at the same time.Based on Joukowski conformal transformation,airfoil geometry is parameterized firstly.Then,the optimization model taking the lift-to-drag ratio and airfoil self-noise as the design objective,is established to modify the airfoil by active set algorithm until the airfoil can satisfy the design condition.Finally,the noise of the optimized airfoil is verified according to the prediction theory of airfoil noise.Moreover,the relationship between airfoil geometry and noise is analyzed.The results show that the lift-to-drag ratio of the optimized airfoil increased,and the noise also decreased.Thus,the optimization method can be used to address special design of low-noise airfoil.Besides,the optimization method in this paper can provide reference for improving lift-to-drag ratio and reducing noise of the airfoil in aircraft and submarine rudder system.

High efficient Al: ZnO based bifocus metalens in visible spectrum

The optical components of the visible light band are widely used in daily life and industrial development. However due to the serious loss of light and the high cost, the application is limited. The broadband gap metasurface will change this situation due to its low absorption and high efficiency. Herein, we simulate a size-adjustable metasurface of the Al doped ZnO (AZO) nanorod arrays based on finite difference time domain method (FDTD) which can realize the conversion of amplitude polarization and phase in the full visible band. The corresponding theoretical polarization conversion efficiency can reach as high as 91.48% (450 nm), 95.27% (530 nm), and 91.01% (65 nm). The modulation of focusing wavelength can be realized by directly adjusting the height of the AZO nanorod. The designed half-wave plate and metalens can be applied in the imaging power modulation halfwave conversion and enriching the spectroscopy.

Numerical investigation of Weber number and gravity effects on fluid flow and heat transfer of successive droplets impacting liquid film

Droplet-based high heat flux dissipation technique under multi-gravitational environments has gained increasing research attention due to the increased requirements of heat dissipation in advanced air-/space-borne electronics.In this paper,a threedimensional model was developed to investigate the impact of continuous droplets on liquid film under various Weber numbers and gravity loads.In other words,the effects of Weber number and gravity load on the flow and heat transfer characteristics were investigated.The results demonstrated that the dissipated heat flux was positively correlated with both Weber number and gravity load.A large Weber number indicated larger kinetic energy of a droplet,leading to a greater disturbance on the impacted film area.When the Weber number was doubled,the average wall heat flux could be enhanced by 36.3%.In addition,the heat flux could be boosted by 5.4%when the gravity load ranged from 0 to 1g.Moreover,a weightless condition suppressed the vapor escape rates on the heating wall where the volume fraction of the vapor on the wall could increase by 20%under 0g,leading to deteriorated heat transfer performance.The novelty in this paper lies in the accurate three-dimensional modeling of an aerospaceoriented droplet impacting two-phase heat transfer and fluid dynamics,associating macro-scale thermal performance to microscale thermophysics mechanisms.The findings of this study could guide the development of aerospace-borne spray cooling facilities for advanced aerospace thermal management.

Advances in ground heat exchangers for space heating and cooling:Review and perspectives

As a renewable energy source,geothermal energy has been widely used to provide space heating and cooling for buildings.The thermal performance of ground heat exchanger(GHE)is significant for the operating efficiency of the ground source heat pump(GSHP)systems.This paper presents a comprehensive review of developments and advances of three kinds of GHE,including vertical borehole GHE(VBGHE),Pile GHE(PGHE),and deep borehole GHE(DBGHE)which are currently popular in larger GSHP systems.Firstly,analytical models proposed to ana-lyze heat transfer process of VBGHE with different geological conditions are summarized,such as homogenous or heterogeneous ground,with or without groundwater advection.Numerical and short-time step models and measures to improve GHE thermal performance are also reviewed.Secondly,a summary of research advances in PGHE is provided,which includes the heat transfer models of PGHE,the effects of geometric structure,oper-ation modes,pile spacing,use of phase change material(PCM),thermal properties of PCM,thermo-mechanical behavior and/or thermal performance of PGHE.The effects of groundwater flow direction and velocity on PGHE are also summarized in brief.Lastly,models of three kinds of DBGHEs,i.e.,deep coaxial GHE(DCGHE),deep U-bend GHE(DUGHE)and super-long gravity heat pipe(SLGHP),are reviewed.The physical bases of the dif-ferent analytical models are elaborated and also their advantages and disadvantages are described.Advances in numerical modelling and improving numerical model calculation speed of DCBHE,DCBHE array,and DUBHE are summarized.The review provides a meaningful reference for the further study of GHEs.

Numerical evaluations on the effects of thermal properties on the thermo-mechanical behaviour of a phase change concrete energy pile

Energy pile is a kind of economic and efficient geothermal utilization technology that the ground heat exchanger(GHE)used in ground source heat pump(GSHP)is embedded into building pile foundation to realize heat ex-change with surrounding soil.Adding phase change material(PCM)into the energy pile can not only reduce the temperature variation and thermal deformation range of energy pile,but also improve its energy storage and heat transfer performance.In this work,phase change concrete energy pile(PCCEP)is proposed by using PCM as a part of backfill material of energy pile.A three-dimensional numerical model is developed to find the influences of thermal properties of PCCEP on its thermo-mechanical behaviour.According to the model,the in-fluences of thermal conductivity,phase change latent heat(PCLE)and phase change temperature(PCT)on the thermal performance and mechanical characteristics of PCCEP are numerically investigated.It can be seen that for improving heat transfer performance of PCCEP,the thermal conductivity should be increased,but from the perspective of reducing change of pile displacement,axial force and side friction resistance,the thermal conduc-tivity should be reduced.Under heat release mode,lower PCT and larger PCLE contribute to the improvement of thermal performance of PCCEP,and accordingly the rise range of pile temperature,pile displacement change and pile thermal stress can all be reduced.The experimental validation on the model shows that the simulation values of pile wall middle temperature(PWMT)and pile top displacement are agreed well with the corresponding experimental results,the real-time relative error of PWMT and pile top displacement are respectively within 5.1 and 12%.

Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals

A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between.The static nonlinear element is modeled by a neural fuzzy network(NFN)and the two linear dynamic elements are modeled by an autoregressive exogenous(ARX)model and an autoregressive(AR)model,separately.When the system input is Gaussian signals,the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element.First,based on the input and output of Gaussian signals,the correlation analysis technique is used to identify the input linear element and output linear element,which addresses the problem that the intermediate variable information cannot be measured in the identified Wiener–Hammerstein system.Then,a zero-pole match method is adopted to separate the parameters of the two linear elements.Furthermore,the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals,which avoids the impact of output noise.The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process.Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.

Image-Based Flow Prediction of Vocal Folds Using 3D Convolutional Neural Networks

In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D CNN model is composed of the feature extraction block and regression block.The feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow data.The input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration modes.The output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal surface.The 3D CNN model is built to establish the mapping between the input image data and output flow data.The ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary solver.The proposed model is trained to predict the concerned flow variables for glottal shapes in the test set.The present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be provided.The prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.

Study on irradiation effect and damage mechanism in cascode GaN HEMT irradiated by 10 MeV electron

This study investigated the irradiation effect of cascode-structure GaN HEMT(High Electron Mobility Transistor)devices,employing high-energy electrons with an energy of 10 MeV and irradiation doses ranging from 5 to 80 Mrad(Si).The variation of electrical properties of the device under annealing condition was analyzed.Geant4 and TCAD simulations were used to analyze the irradiation effect and damage mechanisms.The results show that the threshold voltage has obvious negative drift and the drain current increases after irradiation.The threshold voltage deviation amplitude of the device increases with the increase of irradiation dose,and the maximum deviation is 1.41V.Annealing at high temperatures(80℃,120℃ and 145℃)partially restores the electrical properties,with a 0.49 V restoration in threshold voltage at 145℃.Geant4 simulations reveal that enhanced Si MOSFET is more susceptible to total dose effects.TCAD simulations of enhanced Si MOSFET devices demonstrate an increase in electric field intensity,trapped electron concentration,and hole concentration in Si and SiO_(2) layers with the increase of irradiation dose.These findings can provide support for the space application and irradiation hardening of cascode GaN HEMT devices.