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The underlying structural origin of magnetic properties is still elusive in Fe-based amorphous alloys. In this study, distinctive soft magnetic properties were developed in Fe76Si9B10P5 amorphous ribbons through systematic design of annealing process. Combining with synchrotron radiation, high-resolution transmission electron microscopy and first principle ab initio molecular dynamic simulation, it is found that the atomic structural evolution both in short range order and medium range order is responsible for the magnetic softness at proper annealing temperature. In short range, formation of separated and densely coordinated Fe-metalloid clusters is instigated to adapt energy minimization, resulting in strengthening of ferromagnetic exchange interaction locally. In medium range, a homogeneous exchange-coupling from the uniformly strong and weak ferromagnetic regions is generated, which significantly weakens magnetic heterogeneity and leads to the excellent magnetic softness. Our findings may provide an effective/promising pathway to modulate the magnetic properties for Fe-based amorphous alloys, and give a comprehensive and quantitative understanding of the structure-properties relationship in amorphous materials.

Depression is a mental disorder characterized by persistent low mood that affects a person's thoughts, behavior, feelings, and sense of well-being. Depression will become the second major life-threatening illness in 2020. Electroencephalogram (EEG) signals are regarded as the best physiological tool for depression detection. Previous studies used the Empirical Mode Decomposition (EMD) method, which can deal with the highly complex, nonlinear and non-stationary nature of EEG, to extract features from EEG signals. However, for some special data, the neighboring components extracted through EMD could certainly have sections of data carrying the same frequency at different time durations. Thus, the Intrinsic Mode Functions (IMFs) of the data could be linearly dependent and the pre-proposed EMD-based features could not be extracted. To solve this problem, an improved EMD applying Singular Value Decomposition (SVD)-based feature extraction method was proposed in this study, which can extract the features coefficients of expansion based on all IMFs, ignoring potentially linear dependence of IMFs. Experiments were conducted on four EEG databases for detecting depression. The improved EMD-based feature extraction method can extract feature on the four EEG databases. The average classification results of the proposed method on the four EEG databases reached 83.27%,85.19%,81.98% and 88.07%, respectively.

A unique adaptive neural network control scheme is proposed for active suspension systems by employing bioinspired nonlinear dynamics, so as to address several critical engineering issues including energy efficiency, input delay, and unknown/uncertain dynamics simultaneously. A novel constructive predictor is firstly designed to solve the effect of input delay. Neural networks are then adopted to approximate the uncertain/unknown dynamics, and importantly, a unique finite-time adaptive control is established which can not only online update the input and output weights of the neural networks but also intentionally introduce beneficial nonlinear dynamics to vibration control. The significant difference from most existing controllers lies in that the designed controller can effectively utilize beneficial nonlinear stiffness and damping characteristics of a novel bioinspired reference model and, thus, purposely achieve superior vibration suppression and obvious energy-saving performance simultaneously. Theoretical analysis and experimental results vindicate that the proposed controller can effectively suppress vibration with much more improved control performance and considerably reduced control energy consumption more than 44%. This should be for the first time to reveal both in theory and experiments that a superior suspension performance is achieved simultaneously with an obvious control energy saving, by employing beneficial bioinspired nonlinear dynamics, compared to most traditional control methods.

This paper presents a comparative analysis of thermal and mechanical behaviors of the thermally stratified heat storage system using three different heat transfer materials, which are the binary nitrate salt, ternary nitrate salt and liquid lead–bismuth eutectic (LBE) alloy. The discharging behaviors of the tank using three different heat transfer materials are investigated by using both the algebraic and numerical models. For all the three materials, the algebraic results are consistent with the simulation ones. During the discharging process, in comparison, the thermal stratification thickness of the tank using the binary nitrate salt is relatively smaller, which can lead to a better discharging performance. The comparison results of expanding behaviors of the standby tank reveal that the thermal stratification thicknesses of tanks using the three different heat transfer materials all increase with the standby time increased. Compared with the other two materials, the thermal stratification thickness of the tank using the binary nitrate salt is the smallest at the same standby time. The comparison results of mechanical behaviors of the tank demonstrate that the maximum mechanical stress of the tank using the liquid LBE is the greatest, while the tank using the molten salts can have better mechanical properties.

Fluorine-doped graphene quantum dots have unique chemical bonds and charge distribution, which can bring unexpected properties compared to other common atom-doped graphene quantum dots. In the present work, fluorine and nitrogen co-doped graphene quantum dots (F, N-GQDs) are synthesized from levofloxacin via a simple hydrothermal method. Systematic studies demonstrate that F, N-GQDs can emit various fluorescence with the wavelength ranging from blue to green by dispersing F, N-GQDs into different solvents. Moreover, multi-color fluorescence is available by simply changing the concentration of F, N-GQDs. In addition to these unique characteristics, F, N-GQDs also exhibit a sensitive fluorescence response to tetracycline with an ultralow detection limit of 77 nM in water. Because of high photostability and high quantum yield, the F, N-GQDs are exploited as a unique invisible ink, which is printable and writable on paper. Meanwhile, based on the solvatochromism of F, N-GQDs, we realized the color adjustable fluorescent ink. Finally, large-area flexible multi-color fluorescent films are realized. Our synthesized F, N-GQDs, with tunable fluorescence in wavelength and intensity, have numerous opportunities for optical molecular sensors, information security, flexible optics, and others.

Solar steam generation is considered to be an effective strategy to alleviate the global water shortage problem. Therefore, exploring highly efficient and thermal stability photothermal conversion materials is highly essential and urgent. In this work, we develop a three-dimensional (3D) oxygen vacancy-rich WO3 with ‘‘nanorod array grown on nanosheet array” unique architecture decorated on Ni foam (denoted as WO3−x/NF) through a simple and effective hydrothermal method followed by an annealing route, which is applied as light-absorbing material. The 3D hierarchical porous unique structure of the WO3−x/NF evaporator can supply a channel steam escaping and enhance the light trapping due to the multi-scattering effect, and the localized surface plasmon resonance (LSPR) effects of WO3−x also contribute to increase the light absorption in the full solar spectrum. The as-prepared WO3−x/NF evaporator reveals a high solar absorption (95%), an evaporation rate of 1.50 kg m−2 h−1 under one sun illumination, and a light-to-heat conversion efficiency of about 88%, as well as stable salt-resistance performance. The water purification results show that WO3−x/NF evaporator has a significant effect on seawater desalination without significant salt accumulation and purification of heavy metal wastewater. Furthermore, the first-principles calculations reveal that WO3 with oxygen vacancies has a narrower bandgap, which is more conducive to absorb solar energy from the whole spectrum. This work can provide a new avenue toward the design of other high photothermal conversion system.

The indoor low-concentration formaldehyde (HCHO) removal in cobalt-based catalysts is still a “hot potato”. In this work, metal–organic-frameworks (MOF)-derived and Zinc (Zn)-modulated new cobalt nanoparticles catalyst (CZ-Co@NC-800) was designed and prepared. The CZ-Co@NC-800 performed outstanding elimination activities for ~1 ppm HCHO at 25 °C. In the static test condition, it achieves complete HCHO removal in 3 h at a relative humidity (RH) of ~55%. Moreover, 90.18% HCHO removal ratio is held after five recycle tests. In the dynamic test condition, it remains the characteristic to eliminate around 95.89% of HCHO within 8 h under an RH of ~55% and a gas hourly space velocity (GHSV) of ~150,000 mL·h−1g−1. Such advanced results should be ascribed to large specific surface area bringing about more cobalt active sites; and it is also because residual Zn metal affects the electronic structure of CZ-Co@NC-800 and enhance the surface charge transfer rate, thus the activation and dissociation ability of oxygen is promoted. Besides, a short HCHO reaction path over CZ-Co@NC-800 which was clarified by the In situ DRIFTs is also a reason for excellent catalytic performance. This work represents a crucial addition to expand the family of cobalt-based catalysts for indoor HCHO elimination.

The rational design of oxygen vacancies and electronic microstructures of electrode materials for energy storage devices still remains a challenge. Herein, we synthesize nickel cobalt-based oxides nanoflower arrays assembled with nanowires grown on Ni foam via the hydrothermal process followed annealing process in air and argon atmospheres respectively. It is found that the annealing atmosphere has a vital influence on the oxygen vacancies and electronic microstructures of resulting NiCo2O4 (NCO-Air) and CoNiO2 (NCO-Ar) products, which NCO-Ar has more oxygen vacancies and larger specific surface area of 163.48 m2/g. The density functional theory calculation reveals that more oxygen vacancies can provide more electrons to adsorb -OH free anions resulting in superior electrochemical energy storage performance. Therefore, the assembled asymmetric supercapacitor of NCO-Ar//active carbon delivers an excellent energy density of 112.52 Wh/kg at a power density of 558.73 W/kg and the fabricated NCO-Ar//Zn battery presents the specific capacity of 180.20 mAh/g and energy density of 308.14 Wh/kg. The experimental measurement and theoretical calculation not only provide a facile strategy to construct flower-like mesoporous architectures with massive oxygen vacancies, but also demonstrate that NCO-Ar is an ideal electrode material for the next generation of energy storage devices.

In sixth-generation (6G) communication networks, ultrahigh-data rate and reliability are greatly vital for massive user connections and network sensors, such as Internet of Things (IoT) devices. Simultaneous wireless information and power transfer (SWIPT) has been evolved as an efficient strategy to enhance the reliability of wireless communication systems through prolonging the battery lifetime by harvesting energy from the received radio-frequency (RF) signals. Furthermore, cooperative relay sensors in IoT networks can extend the network coverage. In this article, we consider a massive multiple-input–multiple-output (MIMO) two-way relaying system, where the relay node splits the received RF signals into two power streams, one for information decoding (ID) and the other for energy harvesting (EH). Two classical and linear relay precodings, i.e., zero-forcing reception/zero-forcing transmission (ZFR/ZFT) and maximum-ratio combining/maximum-ratio transmission (MRC/MRT), are adopted to satisfy the requirements of high rate in this relay system. Different from prior work, the SWIPT technique and large-scale fading effects of MIMO channels are taken into account for deriving the asymptotic sum-rates of four prevalent power scaling cases when the number of relay antennas grows to infinity. Finally, the analytical results are evaluated by the presented simulation and numerical results.

Solar-based power and desalination system using super-critical carbon dioxide (S–CO2) Brayton cycle can be considered as one of the most promising development directions of future energy. This study presents the design and behaviour estimate of a novel concentrated solar-driven power and desalination (CSPD) system using S–CO2 Brayton cycle and multi-stage flash (MSF) desalination technology. Operation simulations and exergic analysis of the CSPD system are conducted by utilizing the Ebsilon software. Operation behaviour estimate results of the CSPD system show that the efficiency of S–CO2 Brayton cycle is 36.6%. The electric power and freshwater daily output of the CSPD system are 50.1 MW and 4050.8 t. The CSPD system can perform the solar-driven electricity production and solar-driven desalination simultaneously according to the pre-set operation strategy. The exergic analysis results indicate that the solar tower receiver and heat exchanger of desalination system have the two maximum energy destructions as well as the two lowest exergy efficiencies. The economic analysis results reveal that the CSPD system has an equivalent LCOE (levelized cost of electricity) of 0.059 $∙kWh−1 and an LCOW (levelized cost of water) of 1.15 $∙t−1, which means the CSPD system is economically feasible.

Methanol synthesis from CO2 hydrogenation catalyzed by Zn/Cu alloy has been widely studied, but there is still debate on its catalytic active phase and whether the Zn can be oxidized during the reaction process. What is more, as Zn atoms could locate on Zn/Cu alloy surface in forms of both single atom and cluster, how Zn surface distribution affects catalytic activity is still not clear. In this work, we performed a systematic theoretical study to compare the mechanistic natures and catalytic pathways between Zn single atom and small cluster on catalyst surface, where the surface oxidation was shown to play the critical role. Before surface oxidation, the Zn single atom/Cu is more active than the Zn small cluster/Cu, but its surface oxidation is difficult to take place. Instead, after the easy surface oxidation by CO2 decomposition, the oxidized Zn small cluster/Cu becomes much more active, which even exceeds the hardly-oxidized Zn single atom/Cu to become the active phase. Further analyses show this dramatic promotion of surface oxidation can be ascribed to the following factors: i) The O from surface oxidation could preferably occupy the strongest binding sites on the center of Zn cluster. That makes the O intermediates bind at the Zn/Cu interface, preventing their too tight binding for further hydrogenation; ii) The higher positive charge and work function on the oxidized surface could also promote the hydrogenation of O intermediates. This work provided one more example that under certain condition, the metal cluster can be more active than the single atom in heterogeneous catalysis.

Constructing electrocatalysts with plentiful active sites, great mass transfer ability, and high electrical conductivity is critical to realize efficient hydrogen evolution reaction (HER). Hierarchically porous cobalt phosphide/N-doped nanotubular carbon networks (CoP/NCNs) that have all the features were fabricated in this work. For the fabrication, the polymeric worm-like micelles (PWs) with a large aspect ratio were coated by a uniform nanolayer of Zn-Co zeolitic imidazolate frameworks (Zn-Co-ZIFs) on their surface, resulting in the hybrid nanofibers PWs@Zn-Co-ZIFs (HPWs). Inheriting the randomly curved and entanglement properity of PWs, the rigid HPWs formed hybrid networks with the packing voids sized tens to 200 nm. Then, the hybrid networks were treated by pyrolysis-oxidation-phosphidation and ZnO-removal processes, leading to the hierarchically porous CoP/NCNs. In the CoP/NCNs, there are plentiful CoP nanoparticles embedded on the surface of conductive carbon network and fully exposed. When used for HER electrocatalyst, the CoP/NCNs only need small overpotentials (98 and 118 mV in acid and alkaline electrolyte) at 10 mA cm−2. This novel strategy is instructive for tailoring hierarchically porous transition metal phosphide/carbon nanocomposites as promising electrocatalysts.

The development of high–efficiency and low–cost bifunctional oxygen electrocatalysts is critical to enlarge application of zinc–air batteries (ZABs). However, it still remains challenges due to their uncontrollable factor at atomic level during the catalysts preparation, which requires the precise regulation of active sites and structure engineering to accelerate the reaction kinetics for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Herein, a novel Co–doped mixed lanthanum oxide and hydroxide heterostructure (termed as Co–LaMOH|OV@NC) was synthesized by pyrolysis of La–MOF–NH2 with spontaneous cobalt doping. Synergistic coupling of its hollow structure, doping effect and abundant oxygen vacancies creates more active sites and leads to higher electroconductivity, which contribute to the better performance. As employed as a bifunctional oxygen electrocatalyst, the resulting 3Co–LaMOH|OV@NC exhibits superior electrocatalytic activity for both ORR and OER. In assembled ZAB, it also demonstrates an excellent power density of 110.5 mW cm−2, high specific capacity of 810 mAh gZn−1, and good stability over 100 h than those of Pt/C + RuO2. Density functional theory (DFT) calculation reveals that the heterointerfaces coupled with oxygen vacancies lead to an enhanced charge state and electronic structure, which may optimize the conductivity, charge transfer, and the reaction process of catalysts. This study provides a new strategy for designing highly efficient bifunctional oxygen electrocatalysts based on rare earth oxide and hydroxides heterointerface.

The erosion of soluble rock and transformation of groundwater result in the high geometrical irregularity of cavities in nature. The purpose of this paper is to quantitatively describe the shape of irregular cavities using Fourier transform analysis. Firstly, multi-level characterization indexes of the 2D section of cavities have been proposed, considering the high shape irregularity of cavities. The first level indexes include Shape Factor (SF), Aspect Ratio (AR) and Sphere Like (SL). The second level indexes consist of Angularity Factor (AF) and Convexity Factor (CF). The third level indexes are Roundness Factor (RF). Then, the geometric contour of cavity was transformed into a 2D waveform, with an equal-angle points sampling of the cavity boundary. The geometric boundary of irregular cavity was segmented and transformed from discrete time domain to frequency domain based on discrete Fourier transform (DFT). Furthermore, we proposed Fourier shape descriptor, which was found to control the cavity shape. The relationship between Fourier shape descriptors in different sequences and multi-level characterization indexes was determined using designed and irregular nature cavities as sample data. The results demonstrated that the Fourier shape descriptors​ D 2 , D 3 and D 8 mainly control multi-level characterization indexes including the overall shape, the irregularity and the roughness of the cavity boundary, respectively. Finally, the 3D characterization method of cavity shape was further proposed based on averaging the 2D sections, which hence offers a possible way to further quantitatively study the mechanical properties of surrounding rockmass effected by the irregular shape of cavities.

New high-performance materials have attracted much attention due to ever-increasing demands for advanced communication technologies. In present work, Ge-doped Li3+xMg2Nb1-xGexO6 (0 ≤ x ≤ 0.08) ceramics are prepared via solid-state reaction route. Microstructural analysis and crystal structure refinement reveal that moderate substitution can promote grain growth and modify crystal structure, thus enhancing microwave dielectric properties of composites. In that sense, special attention is paid to the behavior of dielectric constant er, quality factor Q×f, and frequency temperature coefficient τf of final products. In these systems, er parameter depends on the density, miscellaneous phases, and polarizability; Q×f value is shown to be influenced by Nb-O bond energy, grain size, and bulk density; finally, τf characteristic refers to Nb-O bond valence and NbO6 octahedral distortion. Among above ceramics, Li3.02Mg2Nb0.98Ge0.02O6 composite sintered at 1250 °C exhibits outstanding microwave absorption performance with er = 15.32, Q×f = 969 88 GHz, and τf = −8.25 ppm/°C.

Monolithic bulk metallic glass and glass matrix composites with a relative density above 98 % were produced by processing Cu46Zr46Al8 (at.%) via selective laser melting (SLM). Their microstructures and mechanical properties were systematically examined. B2 CuZr nanocrystals (30–100 nm in diameter) are uniformly dispersed in the glassy matrix when SLM is conducted at an intermediate energy input. These B2 CuZr nanocrystals nucleate the oxygen-stabilized big cube phase during a remelting step. The presence of these nanocrystals increases the structural heterogeneity as indirectly revealed by mircrohardness and nanoindentation measurements. The corresponding maps in combination with calorimetric data indicate that the glassy phase is altered by the processing conditions. Despite the formation of crystals and a high overall free volume content, all additively manufactured samples fail at lower stress than the as-cast glass and without any plastic strain. The inherent brittleness is attributed to the presence of relatively large pores and the increased oxygen content after selective laser melting.

The work analyzes dynamic responses of a healthy plant under optimal switching data-injection attacks on sensors and develops countermeasures from the vantage point of optimal control. This is approached in a cyber-physical system setting, where the attacker can inject false data into a selected subset of sensors to maximize the quadratic cost of states and the energy consumption of the controller at a minimal effort. A 0–1 integer program is formulated, through which the adversary finds an optimal sequence of sets of sensors to attack at optimal switching instants. Specifically, the number of compromised sensors per instant is kept fixed, yet their locations can be dynamic. Leveraging the embedded transformation and mathematical programming, an analytical solution is obtained, which includes an algebraic switching condition determining the optimal sequence of attack locations (compromised sensor sets), along with an optimal state-feedback-based data-injection law. To thwart the adversary, however, a resilient control approach is put forward for stabilizing the compromised system under arbitrary switching attacks constructed based on a set of state-feedback laws, each of which corresponds to a compromised sensor set. Finally, an application using power generators in a cyber-enabled smart grid is provided to corroborate the effectiveness of the resilient control scheme and the practical merits of the theory.

Al-TUD-1 (AT) material was successfully prepared via sol-gel method and modified by phosphoric acid. A series characterization of NiMoP/AT catalysts results indicated that after the addition of phosphoric acid, the NiMoP/AT-x series catalysts still retained the large specific surface area and pore structure, which accommodated the active metals for better dispersion. Phosphoric acid could effectively ameliorate the acidity of catalyst and modulate metal-support-interaction (MSI), then the molybdenum species were sulfided to transform into octahedral MoS2 active species with higher degree of sulfurization and dispersion degree. Catalytic performances of different catalysts were estimated using Dibenzothiophene (DBT) and Fluid Catalytic Cracking (FCC) diesel as feedstock, The NiMoP/AT-2 exhibited the maximal DBT hydrodesulfurization (HDS) efficiency of 98.6 % and the highest values of HDS (99.1 %) and hydrodenitrogenation (HDN) (98.5 %) efficiencies for FCC diesel.

Ni2+ modified MgTa2O6 ceramics with a trirutile phase and space group P42/mnm were obtained. The correlations between crystallographic characteristics and microwave dielectric performance of MgTa2O6 ceramics were systematically studied based on the chemistry bond theory (PVL theory) for the first time. The results indicate that the introduction of Ni2+ causes a change in polarizability and the Mg–O bond ionicity, which contributes to the variation of dielectric constant. Moreover, the lattice energy, and packing fraction, full width at half maximum of the Raman peak of Ta–O bond, as the quantitative characterization of crystallographic parameters, regulate the dielectric loss of MgTa2O6 ceramics in GHz frequency band. In addition, the study of sintering behavior shows that the densification and micromorphology are the crucial factors affecting the microwave dielectric performance. Typically, Ni2+ doping on the A-site of MgTa2O6 can effectively promote the Q × f values to 173,000 GHz (at 7.43 GHz), which ensures its applicability in 5G communication technology.

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

To improve the properties of high alumina castables containing calcium aluminate cement (CAC) after firing at elevated temperatures, micro-sized basic zinc carbonate (BZC) was introduced as ZnO-based nano fragments into castables. To account for the influence of BZC on the evolution of castable properties, the phase composition and microstructure of castable matrices were examined with X-ray diffraction and scanning electron microscopy equipped with energy dispersive spectroscopy, respectively. Properties of castables having BZC were compared with those with Zn(OH)2. Results show that strength of castables added with BZC after firing at both medium temperatures and 1550 °C was improved. The volume stability and hot modulus of rupture at 1550 °C of fired castables containing BZC were also enhanced.

The effects of Cd2+ ions on the microstructure, magnetic properties, and dielectric properties of Bi2O3-added MgFe2O4 ferrites (CdxMg1-xFe2O4, x = 0.00, 0.15, 0.30 and 0.45) are obtained by adopting the solid-state reaction method at a low temperature (910 °C). The objective is to achieve matching impedances, low magnetic and dielectric losses (tan δμ and tan δe, respectively), and a relatively large miniaturization factor to reduce antenna size. Experimental results indicate that the cations occupying the tetrahedral (A) and octahedral (B) ion sites are redistributed, resulting in an enhanced super-exchange interaction between the two sublattices. As a result, improved magnetization, including the increase in saturation magnetization (41.74 emu/g) and decrease in coercivity (63.75 Oe), is realized. The real part of permeability (µ′) also increases with increasing concentration of Cd2+ ions. When x is 0.15, matching impedances with equivalent μ′ and e′ values are obtained over a long frequency range (1–150 MHz). Moreover, the formation of a dense microstructure guarantees that losses occur at low orders of magnitude (tan δμ ≈ 10−2 and tan δe ≈ 10−3). Accordingly, these properties afford wide application perspectives for the proposed compounds in the high-frequency region, i.e., from high-frequency to very-high-frequency bands.

A four-way enhanced numerical manifold method (NMM) is proposed in order to simulate crack propagation in rock media and progressive failure of jointed rock slopes. Specifically, the original NMM is improved in the following four aspects: (1) An “enriched local approximation” is employed to characterize singularity and discontinuity of the displacement/stress fields near the crack tip. (2) A “modified generalized Duffy transformation” is proposed to improve the accuracy of numerical integration associated with the 1/r and 1 / r singularities. (3) A “critical length cracking criterion” is introduced to determine propagation length and direction for both tensile-shear and compressive-shear cracks in rock mass. (4) A “cone-complementary-based contact formulation” is incorporated to resolve the cohesive/frictional contact phenomena between discontinuous interfaces more accurately. Some examples demonstrate the performance and application prospect of the presented NMM in stability and failure analysis of the jointed rock slopes.

Deep learning and radar make it feasible to automatically recognize human activities in various lighting conditions, even occlusion case, which significantly promotes the application of activity recognition in the fields of security surveillance, health care, and so on. In this paper, an approach for human activity recognition (HAR) using deep learning is proposed based on stepped frequency continues wave (SFCW) radar. Specifically, SFCW radar is utilized to generate two types of characteristic representation domains, namely multiple frequencies of spectrograms in time–frequency domain and range maps in range domain. On the one hand, spectrograms and range maps provide different types of features. On the other hand, multi-frequency spectrograms furnish same type of features while with different scattering properties and frequency resolutions. Then a specific deep learning network including multiple parallel deep convolutional neural networks (DCNNs) and a sparse autoencoder is designed to extract and fuse these features associated with human activities from the multi-frequency spectrograms and rang map. In particular, each DCNN is aimed at extracting the detailed micro-Doppler features from a spectrogram, while sparse autoencoder learns prime range distribution features by compressing each range map to reduce complexity and improve robustness. Experimental results verify that the proposed deep learning scheme achieves 96.42% recognition accuracy about six types of activities by incorporating three frequencies of spectrograms and range map, and surpasses two existed methods depending on single-frequency spectrogram and combination of single-frequency spectrogram and range map.

Hydrogen (H2) energy is a promising candidate to replace carbon monoxide (CO) as a reductant for iron oxide reduction in revolutionary ironmaking industrial processes, and numerous studies have been conducted to intensively study the utilization and impact of H2 in ironmaking processes. Therefore, this review first collects and compares the H2-assisted reduction mechanism and kinetics. The impacts of H2 on the reduction accompanying behaviors, such as the disintegration, swelling, sticking, softening, and melting of iron ores, are then summarized. Third, the performance of H2 predicted by either mass and heat balance models or numerical simulation models in various ironmaking processes is highlighted. Finally, the different applications of hydrogen-rich materials in blast furnace and non-blast furnace ironmaking processes are further compared to illuminate H2 utilization before our outlook on the use of H2 in the ironmaking industry.

Inspired by the advantages from natural birds’ shape-morphing adapted flight performance, this work investigates how the bioinspired adaptative morph induced inertia variation affects the flight performance of a morphing quad-rotor. Extensive numerical and experimental evaluations on a custom-built morphing quad-rotor that is steered along a predefined path and hovers in the presence of constant wind disturbance were performed and analyzed. Simulation results demonstrate that the smaller quad-rotor exhibits better agility performance due to the compact volume/size and lower weight, while the bigger one appears more flight robustness in a disturbed environment such as windy testing area. Experimental studies further prove that by adaptatively altering its volume/size, our quad-rotor with the bioinspired in-flight morphing behavior can well execute path following tasks in a constrained space without deviating the trajectory when encountering obstacles, as well it can withstand external wind torque through morphing up its volume to therewith increase relevant moment-of-inertia for improving the flight robustness. Compared to the regular scaled aerial vehicles whose volumes’ change follows a weight change proportionally, our results also indicate that our in-flight morphing aerial vehicle mechanism (i.e. the quad-rotor morphs the volume but without changing weight/mass) shows more advantages in power efficiency and morphologically adaptative flight capability.

Semiconcept, a new data processing model, enriches the development of formal concept analysis. However, the classical semiconcept is supported by two-way decisions. In the study of classical semiconcept theory, how to express the information of jointly not possessed is also essential in making decisions. Therefore, this paper tries to combine the classical semiconcept theory with three-way decisions to present three-way semiconcepts, and carry on the further study. Firstly, we define new operators and give some properties of them. Two kinds of three-way semiconcepts —OE-semiconcept and AE-semiconcept, are presented. And the corresponding structures are searched out from the perspective of lattice theory. Furthermore, we analyze the relationship among three-way concepts, three-way semiconcepts and classical semiconcepts. On this basis, the algorithms to build OE-semiconcept and AE-semiconcept are presented. At the meanwhile, we take some examples to examine and explain the obtained results.

Structure-property relationship for fluorophores with favorable nonlinear optical (NLO) properties are promising topics in organic chemistry and material science. Herein, a series of terthiophene-o-carborane dyads and triads covalently linked with different end-capping styles were readily synthesized and comprehensively investigated. Quantitative values of the crystal and packing structures, photophysical parameters including aggregation-induced emission (AIE) and two-photon absorption (2PA) were provided. Significant impact of carborane unit for introducing the AIE characteristic has been investigated in contrast to the parent oligothiophene. All the obtained fluorophores exhibit maximum absorption around 370 nm in THF and emit bright reddish photoluminscence with absolute fluorescence quantum yields above 16% in solid states. Intramolecular charge communication between oligothiophene and carborane plays important roles in the related NLO properties. These results are supported well by the time-dependent DFT theoretical calculations. Effective 2PA cross sections (δ2PA = 95-355 GM@650 nm) and transition dipole moments of the derivatives are variable for different end-capping styles. Their potential applications as optical limiting materials based on the 2PA mechanism in solutions and doped PDMS films were further evaluated. Taken together, this work provides an understanding of their structure-property relationship, and flexible PDMS films as outstanding candidates for practical applications in optical limiting.

During the refining of Si deoxidized spring steel by low-basicity slag, with the addition of BaCO3 to refining slag, many BaO-containing CaO-SiO2-Al2O3 inclusions existed in steel, which were with approximate chemical compositions to other CaO-SiO2-Al2O3 inclusions. Thermodynamic calculation revealed that (BaO) in slag can be hardly reduced by [Si] in steel, which meant that CaO-SiO2-Al2O3-BaO inclusions in Si-deoxidized spring steel cannot be produced by chemical reactions among slag-steel-inclusion, viz., they were originated from entrapped slag.

The strategy of ternary blend is one of the most important approaches to enhance the power conversion efficiency (PCE) for polymer solar cells (PSCs). The present work prepared a new series of ternary PSCs, PBDB-T-2F:IT-4F:ITIC, based on the perspective of compatibility of non-fullerene molecule (ITIC) and its derivative (IT-4F) and studied the effect of the third component (ITIC) on the morphology and photoelectric conversion process of PBDB-T-2F:IT-4F solar cells. The results showed that surface roughness and phase scale of the PBDB-T-2F:IT-4F changed with the ITIC content, leading to variable morphology, optical, photophysical and electrical characteristics in ternary blend active layers. When the content of ITIC reached 20%, the ternary PSCs achieved the highest PCE (13.41%) among the PSCs. Morphology and optical spectroscopy studies showed that the enhanced performance of ternary device can be attributed to the morphology optimization of ternary active layer with the content of ITIC, and consequently the improved charge photogeneration processes. This work provided another way to achieve the high-performance ternary polymer solar cells via the combination of compatible non-fullerene molecules.

Viscous oil adherence onto solid surfaces is ubiquitous and has caused intractable fouling problems, impairing the function of solid surfaces in various areas such as optics and separation membranes. Materials with superhydrophilicity and underwater superoleophobicity are very effective in elimination of oil fouling. However, most of them cannot dewet viscous oils and may malfunction without prehydration treatment. Herein, we report a facile and environmental strategy to prepare barium sulfate (BaSO4) nanocoating to dewet viscous oils on dry surfaces. Abundant surface polar groups (surface hydroxyl) on BaSO4 nanocoating enhance both hydrophilicity after oil fouling (underoil water contact angle 155°) and then facilitate oil dewetting ability. Different oils with viscosity up to 900 mPa·s can be easily eliminated after immersion into water. The results and force analysis also demonstrate that small surface roughness and ultrahydrophilicity under oil are beneficial to achieve oil dewetting property on dry surfaces. Furthermore, BaSO4 nanocoating displays excellent mechanical, thermal and chemical stability and can maintain oil repellency through various harsh conditions. Outstanding antioil fouling ability also enables the fabric coated by BaSO4 nanocoating to separate crude oil/water with flux higher than 28 000 Lm2-h-1 and separation efficiency larger than 99.9% and maintain effective separation performance even after 100 times of separation. Thus, the robust superhydrophilic BaSO4 nanocoating is potential in oil dewetting and waste oil remediation.

It is confirmed that surfaces with specific microstructures could exhibit good superhydrophobic properties, and there are also a lot of conclusions about droplet hysteresis behavior. However, most of the research methods are based on two-dimensional ideal model and experimental observation at the macroscale. Further research needs to be conducted about the hysteresis behavior of droplets on the microstructure surface under three-dimensional conditions. In this paper, the influence of curvature variation of the liquid surface between pillars on the contact angle hysteresis (CAH) has been investigated. The simulation results were in good agreement with the experimental measurements. Analyses were conducted on the morphology change and force of the liquid surface between pillars, and an index was proposed to describe the degree of difficulty of liquid surface movement. It was revealed that a change in the direction of the surface tension at the three-phase interface caused by curvature variation of the liquid surface between pillars played an important role in the movement of the liquid surface. The greater the surface tension component in the normal direction of the liquid surface, the more likely it was for the liquid surface to advance or recede. The local curvature of the liquid surface increased or the angles between the pillars increased, and the effect of the CAH would be weakened.

The construction of nanostructured ion-transport channels is highly desirable in the design of advanced electrolyte materials, as it can enhance ion conductivity by offering short ion-transport pathways. In this work, we present a supramolecular strategy to fabricate a nanocomposite electrolyte containing highly ordered lamellar proton-conducting nanochannels, by the electrostatic self-assembly of a polyoxometalate H3PW12O40 (PW) and a comb copolymer poly(4-methlstyrene)-graft-poly(N-vinyl pyrrolidone). PW can effectively regulate the self-assembling order of polymer moieties to form a large-range lamellar structure, meanwhile, introducing protons into the nanoscale lamellar domains to build proton transport channels. Moreover, the rigid PW clusters contribute a remarkable mechanical reinforcement to the nanocomposites. The lamellar nanocomposite exhibits a conductivity of 4.3 × 10−4 S/cm and a storage modulus of 1.1 × 107 Pa at room temperature. This study provides a new strategy to construct nanostructured ion-conductive pathways in electrolyte materials.

DC transformer is an essential equipment for formation of multi-voltage-level DC grid. This paper proposes a novel non-isolated Boost-type alternate arm DC transformer (Boost-AADCT) for medium- and high-voltage level DC grid application. The Boost-AADCT is comprised of three submodule-cascaded arms, two thyristor-cascaded director switches, and two buffer capacitors. Bidirectional power transmission is achieved by alternatively charging and discharging of the intermediate arm following the Boost principle, while the pulsating power component caused by intermittent transmission is actively smoothened by grid-side buffer subcircuits formed by the buffer capacitors and grid-side arms. The active power buffering method increases the tolerance range of voltage ripple of the buffer capacitor, and the required energy storage capacity is resultantly reduced. Circuit configuration, operation principle, and mathematical model are first introduced. Then, the control strategy is proposed, the main circuit parameters are designed, and bidirectional fault-blocking capability is analyzed. Comparison with other DC-DC converters shows that the Boost-AADCT significantly reduces the requirement of semiconductors and has relatively lower synthetic cost. At last, performance of the Boost-AADCT and the proposed control strategies are evaluated by simulation tests conducted in MATLAB/Simulink environment.

This letter mainly deals with the problem of counting moving human targets in an enclosed building space for through-the-wall radar. Specifically, a typical deep convolutional neural network, namely, residual neural network (ResNet), is designed to identify the line-like texture information associated with the target number from the blurred range-time images of a single-channel stepped-frequency continuous-wave (SFCW) radar. Experiments demonstrate that the ResNet-based counting algorithm achieves an accuracy of 91.54% for one to six human targets, and the accuracy rises to 97.12% when only counting one to three humans, even under conditions of wall penetration degradation, limited spatial resolution, heavy multipath clutters, and target-to-target occlusion. The achieved number of information of moving human targets not only contributes directly to the situation assessment behind the wall but also can act as the prior information to promote further target detection.

Wolframite-structured Mg1-xCaxZrNb2O8 (0−0.1) ceramics were synthesized through a solid-state procedure. Crystal refinement indicates a pure Mg1-xCaxZrNb2O8 ceramic with a wolframite structure. Ca2+ substitution led to the redshift of the Ag mode at approximately 896 cm−1. Additionally, Ca2+ substitution could promote grain growth and contribute to microstructure evolution from a polyhedral shape to a rod shape. According to chemical bond theory, an appropriate Ca2+ concentration can increase Nb O bond iconicity and Nb O bond lattice energy, which contributed to the great improvement in the dielectric constant and Q × f value. Additionally, the τf value was affected by the bond valence and thermal expansion coefficient of the Mg O bond. The Mg1-xCaxZrNb2O8 (x = 0.04) ceramics exhibited great improvement in the Q × f value: er = 25.21, Q × f = 116,000 GHz (@7.17 GHz) and τf = −24.4 ppm/°C, which provides enormous potential for future millimeter-wave applications.

Today’s video streaming market is crowded with various content providers (CPs). For individual CPs, understanding user behavior, in particular how users migrate among different CPs, is crucial for improving users’ on-site experience and the CP’s chance of success. In this article, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions. Based on a large ISP dataset over two months (6 major content providers, 3.8 million users, and 315 million video requests), we study common migration patterns and reasons of migration. We find that migratory behavior is prevalent: 66% of users switch CPs with an average switching frequency of 13%. In addition, migration behaviors are highly diverse: regardless large or small CPs, they all have dedicated groups of users who like to switch to them for certain types of videos. Regarding reasons of migration, we find CP service quality rarely causes migration, while a few popular videos play a bigger role. Nearly 60% of cross-site migrations are landed to 0.14% top videos. Finally, we validate our findings by building an accurate regression model to predict user migration frequency as well as user survey, and discuss the implications of our results to CPs.

Metal-organic chemical vapor deposition (MOCVD) is a key technique for developing and manufacturing zinc oxide (ZnO) devices. The film quality and performance of these devices are influenced by the process parameters. In this study, the film deposition rate and film uniformity obtained from the simulations and experiments under different process parameters, such as the substrate temperature, operating pressure, mass fraction of the source gas, and inlet flow rate, are compared and verified. In addition, the corresponding temperature field, flow field, distribution of source mass fraction, and velocity field are analyzed using the computational fluid dynamics (CFD) method to explain the influence of the flow field change on deposition rate and film uniformity. This study also presents the correlation test for the film uniformity between the simulation and experimental results; it was found that all the 16 sets of results are significantly correlated and that more than half of them are highly correlated. To ensure the consistency of the experimental results and to prove their reliability, the repeatability test and stability test are conducted.

The Conventional zero-sequence (ZS) overcurrent protection method exhibits poor performance for single-phase to ground faults (SPGFs) with high impedance in low-resistance grounded systems (LRGSs), a type of medium-voltage (MV) distribution system. Hence, in this study, a novel SPGF detection methodology, designed specifically for LRGSs, is proposed. Considering the effects of system imbalance, a crucial limitation to the method based on ZS components, the concept of the Δ-circuit is first utilized to suppress such effects, maintaining the independence of the ZS network. Based on this, the proposed method computes the ZS power factor of a line to discriminate the line state, with the assistance of single-phase instantaneous reactive power theory (SIP) and the 4th-order components of the instantaneous active power. Not only the arcing features, but also the inherent characteristics brought by the unique grounding mode are subtly involved. Multiple simulations conducted in PSCAD software verify the good performance of the proposed detection method for various SPGFs, including those with low or high impedance and those with linear fault impedance or nonlinear arcs.

modular multilevel matrix converter (M3C) has considerable application potential in motor driving and power systems, but its phasor model is rarely studied. This paper proposes a novel method for modelling and analysis of the M3C in the phasor domain. Firstly, an improved phasor method in matrix form is proposed for direct AC/AC MMC family. By including the frequency information into the phasor expression, it is capable of analysis towards interaction between AC variable groups with different frequencies. Secondly, based on the proposed phasor matrix method, the concept of two-dimensional phase-sequence is proposed to facilitate description and calculation of the AC variables in the 3-by-3 matrix-resembled circuit structure of M3C. Network of each phase-sequence is also derived. With the proposed methods, calculation burden for modelling and analysis of the M3C is significantly reduced. Then, the phasor model of M3C considering the DC, fundamental frequency and 2nd-order harmonic components is established. Finally, validation results with time-domain simulation is provided.

DC circuit breakers (DCBs) are expected to isolate faults in high-voltage DC grids. Multiple DCBs are required for a DC bus connected to multiple lines, and they form a DCB cluster. To reduce the construction cost, the DCB cluster can be integrated into a multiport DCB (MDCB). The integration of proactive-DCBs has been widely researched. However, in previous articles, only a few MDCBs have been proposed to integrate current-injection-DCBs (C-DCBs), and the interruption reliabilities of these MDCBs are reduced because multiple mechanical switches (MSs) should be opened during an interruption. This article proposes a novel MDCB to integrate C-DCBs. In the proposed MDCB, all ports share one main breaker and one energy absorber. The interruption operations during a port fault and bus fault are detailed, and the backup protection for an MS failure is presented. The parameter designs of the proposed MDCB are discussed. The proposed MDCB is compared with the DCB cluster to show the economic advantage and is compared with the previous MDCB to show the reliability advantage. Finally, the proposed MDCB is validated using PSCAD/EMTDC simulations.

In this study, Zn2+-substituted Li2MgSiO4 ceramics (Li2(Mg1-xZnx)SiO4, x = 0.00, 0.05, 0.10, 0.15, and 0.20) were synthesized using a traditional solid-state method. A fixed amount of LiF sintering aid (1.5 wt%) was added to the ceramics for decreasing the sintering temperature and adjusting their microwave dielectric properties. X-ray diffraction (XRD) results revealed no secondary phases, and scanning electron microscopy (SEM) data suggest that the Zn2+ ion substitution increased the size and uniformity of the grains, thereby affecting the densification of the prepared ceramics. The maximum bulk density (2.94 g/cm3) was found in a Zn2+ ion-substituted ceramic with x = 0.10 at a relative density of 94.2% (compared with the XRD theoretical density). Excellent microwave dielectric properties (er = 6.28, Q × f = 50400 GHz, and τf = −145 ppm/°C) can also be obtained at this zirconium content. We believe that the developed ceramics are promising for use as antenna substrates or transmit/receive modules in low-temperature co-firing ceramic applications.

Due to its high modularity and scalability, the modular multilevel converter (MMC) has extensively been used in the modern industry. The low reliability of MMCs, which are consisted of tens or hundreds of vulnerable submodules (SMs), directly impacts the power supply continuity of the access systems. Redundancy and preventive maintenance are two significant measures to improve MMC reliability in operation. Therefore, this article presents a reliability model of MMCs with numerous SMs, whose lifetime follows arbitrary distribution, in redundancy strategies, such as active mode and standby mode. The lifetime of SMs is the minimum of component lifetime, which is estimated by a physics-of-failure-based lifetime estimation approach. Employing this reliability model, a reliability-centered maintenance (RCM) scheme with a minimum annual maintenance cost, is proposed to implement the dynamic decision of the maintenance intervals, quantitatively considering multistate of MMC operation. The relationship among redundancy, maintenance, and cost is further discussed in case studies. The proposed reliability model and RCM scheme provide a guideline for MMC redundancy strategy selection and maintenance decision.

This paper proposes a novel system identification scheme to obtain a MIMO model of ship maneuvering motion, which can leverage the temporal correlation from the constructed training data to learn the underlying feasible model robust to extraneous noise. The scheme is based on long–short-term-memory (LSTM) deep neural network, which is more easily trained than traditional feedforward neural network with more complicated network structure. First, multiple datasets of simulated standard maneuvers (10°/10° and 20°/20° zigzag, 35° turning circle) of a KVLCC2 model are artificially polluted with white noise of various levels and used simultaneously to train the deep neural network model. Meanwhile, the data of 15°/15° zigzag maneuver are used to facilitate the training process to alleviate overfitting problem. Second, different datasets of modified zigzag tests are used to validate the generalization performance and robustness to noise of the trained neural network model. The training and validation results demonstrate that a mapping between the dynamics of ship motion and the computation in LSTM deep neural network is correctly identified. This mapping indicates that the complex nonlinear features of ship maneuvering motion can be learned from the measured temporal data, using standard training techniques for deep neural networks. An equivalent LSTM deep neural network model with better generalization performance and robustness is established, and its accuracy in predicting ship maneuvering motion is validated.

Surface-enhanced Raman scattering (SERS) substrates based on graphene and its derivatives have recently attracted attention among those interested in the detection of trace molecules; however, these substrates generally show poor uniformity, an unsatisfactory enhancement factor, and require a complex fabrication process. Herein, we design and fabricate three-dimensional (3D) graphene/silicon (3D-Gr/Si) heterojunction SERS substrates to detect various types of molecules. Notably, the detection limit of 3D-Gr/Si can reach 10-10 M for rhodamine 6G (R6G) and rhodamine B (RB), 10-7 M for crystal violet (CRV), copper(II) phthalocyanine (CuPc), and methylene blue (MB), 10-8 M for dopamine (DA), 10-6 M for bovine serum albumin (BSA), and 10-5 M for melamine (Mel), which is superior to most reported graphene-based SERS substrates. Besides, the proposed 3D-Gr/Si heterojunction SERS substrates can achieve a high uniformity with relative standard deviations (RSDs) of less than 5%. Moreover, the 3D-Gr/Si SERS substrates are reusable after washing with ethyl alcohol to remove the adsorbed molecules. These excellent SERS performances are attributed to the novel 3D structure and abundantly exposed atomically thin edges, which facilitate charge transfer between 3D-Gr and probe molecules. We believe that the 3D-Gr/Si heterojunction SERS substrates offer potential for practical applications in biochemical molecule detection and provide insight into the design of high-performance SERS substrates.

To obtain dense Al-based quasicrystalline (QC) coatings with high content of quasicrystal phase, the QCs were deposited by the high-velocity air fuel spray process with different feedstock powder sizes. The phase composition, microstructure, chemical composition, tribological, and mechanical properties of the coatings were analyzed using x-ray diffraction, scanning electron microscopy, energy-dispersive x-ray spectroscopy, a pin-on-disk wear tester, and a nanohardness tester. The results indicated that smaller particle sizes produced denser structures with lower coating porosities, while the coarser powder helped maintain the desired quasicrystalline phase. In addition, the coatings sprayed with coarser powders exhibited a lower sensitivity to the sliding velocity due to a stronger bonding strength between the splats and a lower oxidation degree. The coating first exhibited abrasive wear coupled with oxidative wear, which was then converted to delamination, abrasive wear, and intensified oxidative wear.

Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of unlabeled data to improve the performance of the model. This paper combines the co-training strategy and random forest to propose a novel semi-supervised regression algorithm: semi-supervised random forest regression model based on co-training and grouping with information entropy (E-CoGRF), and applies it to the evaluation of depression symptoms severity. The algorithm inherits the ensemble characteristics of random forest, and combines well with co-training. In order to balance the accuracy and diversity of co-training random forests, the algorithm proposes a grouping strategy to decision trees. Moreover, the information entropy is used to measure the confidence, which avoids unnecessary repeated training and improves the efficiency of the model. In the practical application of evaluation of depression symptoms severity, we collect cognitive behavioral data of emotional conflict based on the depressive affective disorder. And on this basis, feature construction and normalization preprocessing are carried out. Finally, the test is conducted on 35 labeled and 80 unlabeled depression patients. The result shows that the proposed algorithm obtains MAE (Mean Absolute Error) = 3.63 and RMSE (Root Mean Squared Error) = 4.50, which is better than other semi-supervised regression algorithms. The proposed method effectively solves the modeling difficulties caused by insufficient labeled samples, and has important reference value for the diagnosis of depression symptoms severity.

A surface-modified layer (SML) can play an important role in the durability and in-service behavior of key components, but characterization of the mechanical properties of SML is complex. In this paper, an indentation method to characterize the plastic parameters of the SML is proposed. The method combines indentation tests and finite element simulation to analyze the plastic parameters of the SML. The yield strength and hardening index of the SML for 18CrNiMo7-6 alloy steel, before and after heat treatment, were characterized using the proposed method. After heat treatment, the yield strength and hardening index decreased gradually along the depth. The results obtained agree with the tensile test results.

This letter proposes non-orthogonal multiple access (NOMA) based coordinated direct and relay transmission (CDRT) and hybrid multiple access (HMA) protocols for a general CDRT system, where a base station directly serves cell-center users (CCUs), while it communicates with cell-edge users (CEUs) via a relay. The ergodic sum capacity (ESC), capacity scaling and the number of successive interference cancellation (SIC) operations are derived correspondingly. If the number of CCUs is less than that of CEUs and the interference level of imperfect SIC is small, the proposed NOMA-based CDRT can achieve much better ESC than HMA. Otherwise, the proposed HMA achieves a better performance-complexity tradeoff. Numerical results verify the effectiveness and superiority of the proposed protocols.

Ion doping strategy is a promising method to enhance the efficiency of Cu2ZnSn(S,Se)4 (CZTSSe) solar cells, however, most of the reported works focus on studying the single ion doping in CZTSSe absorber layers. Here, Li+&Ag+ and Li+&Cd2+ double-ion-doping strategy are applied to improve the microstructure and device performances. We found that the double-ion-doping strategy can obviously increase the grain size, reduce the thickness of fine-grain layers and enhance the device performances. Furthermore, the PCEs of Li+&Ag+ and Li+&Cd2+ doped CZTSSe solar cells are up to 8.87% and 8.39%, respectively. It shows an encouraging improvement of over 43.5% and 35.8% enhancement compared with the traditional method (6.18%), and significantly higher than single ion doping strategy (7.60%, 7.99% and 7.11% for Li+, Ag+, and Cd2+ doped CZTSSe solar cells). This work provides a new solution to improve the microstructure and device performances of CZTSSe solar cells.