Stable Architecture Research Articles

Doherty power amplifier with simplified out‐of‐phase power splitter for 5G applications

AbstractThis letter presents the architecture and characterization of a Doherty power amplifier (DPA) with a simplified out‐of‐phase power splitter (SOPPS) for fifth‐generation (5G) small‐cell applications. The proposed SOPPS consists of two passive components, which is conducive to chip miniaturization. Additionally, a stable DPA architecture is employed to avoid oscillation caused by out‐of‐phase ports imbalance. A DPA based on gallium arsenide (GaAs) heterojunction bipolar transistor (HBT) technology is fabricated, achieving high gain of 32 dB over 2.5 to 2.7 GHz. Using a continuous‐wave signal, the DPA exhibits a saturation output power of 36 dBm, with a peak power‐added efficiency (PAE) of 42.3% and 37% at saturation and 6‐dB power‐back‐off, respectively, across the operating band.

Assessing the Architectural Compatibility of Urban Buildings With Passive Defense Principles in Eslamshahr City

Background and objective Urban security is one of the important challenges for urban managers and planners. In this regard, the passive defense approach in urbanization and residential constructions is considered by urban management as a way to cope with natural and man-made disasters and manage the crisis. The present study aims to develop a conceptual model for the architectural compatibility of buildings in Eslamshahr County, Tehran, Iran, with passive defense principles by assessing the compatible and non-compatible elements to provide essential strategies. Method This is a quantitative-qualitative study. The participants were 30 experts in the field of urban security in Eslamshahr. The data analysis was done using statistical tests such as binominal test, one sample T-test, exploratory factor analysis, and strengths, weaknesses, opportunities, and threats (SWOT) technique. Results The results indicated that the constructions in Eslamshahr are not so compatible with the passive defense principles; the level of compatibility was low to moderate. Conclusion The conceptual model was based on four main categories, including stimulating factors, developing factors, leading factors, and supportive factors. The relations between these categories were set according to the structure of a car wheel, which can be the basis for using stable and safe architecture in constructions.

Network Security Prediction and Situational Assessment Using Neural Network-based Method

Technology development has promoted network construction, but malicious network attacks are still inevitable. To solve the problem that the current network security assessment is not practical and the assessment effect is poor, this study proposes a network security monitoring tool based on situation assessment and prediction to assist network security construction. The framework of the evaluation module is based on convolution neural network. The initial module is introduced to convert some large convolution cores into small convolution cores in series. This is to reduce the operating cost, because building multiple evaluators in series can maximize the retention of characteristic values. This module is the optimized form of Elman neural network. The delay operator is added to the model to respond to the time property of network attack. At the same time, particle swarm optimization algorithm is used to solve the initial weight dependence problem. The research adopts two methods of security situation assessment and situation prediction to carry out model application test. During the test, the commonly used KDD Cup99 is used as intrusion detection data. The experimental results of the network security situation evaluation module show that the optimization reduces the evaluation error by 3.34%, and the accuracy meets the evaluation requirements. The model is superior to the back propagation neural network and the standard Elman model. The model proposed in this study achieves better prediction of posture scores from 0.3 to 0.9, which is more stable than BP neural network. It proves that the model designed by the research can achieve more stable and higher prediction than similar models. It is more practical to obtain better results on the basis of a more stable model architecture and lower implementation costs, which is a meaningful attempt in the wide application of network security.

Synergistic influence of carbon nanotube-graphene oxide hybrid and nanosized interfacial TiC on the mechanical performance of Cu matrix composites

Reinforcement architecture and interfacial structure are two key factors affecting the performance of composites. Herein we attempted to achieve a comprehensive improvement of mechanical properties of carbon nanomaterials (CNMs) reinforced Cu matrix composites by means of reinforcement structure construction and interface structure optimization. Graphene oxide (GO) and acid-treated carbon nanotubes (AT-CNTs) were self-assembled into a GO-CNT hybrid structure through an ultrasonic mixing method, and then used as reinforcement of Cu matrix composite through the powder metallurgy method. The strengthening efficiency of GO-CNT hybrid was significantly higher than that of individual CNTs or GO, which could be attributed to the stable interconnected architecture of GO-CNT hybrid formed through sp3 hybridized C–C bonds and the larger interfacial area between them and Cu matrix. In addition, aiming at the issue of poor interfacial adhesion between the GO-CNT hybrid and Cu matrix, TiC nanoparticles were introduced onto the surface of GO-CNT hybrid through a pressureless spark plasma sintering strategy. The TiC nanoparticles can improve the interfacial wettability and optimize the interfacial bonding state. TEM results confirmed a semi-coherent interfacial lattice matching relationship between TiC nanoparticles and Cu matrix, which was conducive to forming robust interfacial bonding. Through the construction of hybrid reinforcement and the introduction of nanosized interfacial TiC, the GO-CNT hybrid reinforced Cu matrix composite has obtained ultra-high strengthening efficiency, and exhibited satisfied combination of strength and ductility. This study can provide a referential strategy for the comprehensive performance optimization of CNMs reinforced metal matrix composites.

Combining Imine Condensation Chemistry with [3,3] Diaza‐Cope Rearrangement for One‐Step Formation of Hydrolytically Stable Chiral Architectures

AbstractDynamic covalent chemistry (DCC) has, in recent years, provided valuable tools to synthesize molecular architectures of increasing complexity. We have also taken advantage of imine DCC chemistry to prepare TPMA‐based supramolecular cages for molecular recognition applications. However, the versatility of this approach has as a major drawback the intrinsic hydrolytic lability of imines, which hampers some applications. We present herein a synthetic strategy that combines the advantages of a thermodynamic‐driven formation of a supramolecular structure using imine chemistry, together with the possibility to synthetize chiral hydrolytically stable structures through a [3,3]‐sigmatropic rearrangement. A preliminary mechanistic analysis of this one‐pot synthesis and the scope of the reaction are also discussed.

Precursor template-induced egg white-derived peptides self-assembly for the enhancement of curcumin: Structure, environmental stability, and bioavailability

Natural multicomponent peptides with abundant bioactivity, varied sizes, and tunable interaction potential are available for rational designing novel self-assembled delivery carriers. Herein, we exploited zein-hyaluronic acid nanoparticles (Z-HA NPs) with a predetermined ordered structure as precursor templates to induce the self-assembly of egg white-derived peptides (EWDP) to generate stable spherical architectures for the enhancement of curcumin (Cur). The resulting Z-EWDP-HA NPs encapsulated hydrophobic Cur through robust hydrogen bonding and hydrophobic interactions with high encapsulation efficiency (97.38% at pH 7.0). The NPs presented superior Cur aqueous solubility, redispersibility, and photothermal stability. More importantly, the self-assembled EWDP could exert synergistic antioxidant activity with Cur and enhance the bioaccessibility of Cur. Meanwhile, the favorable biocompatibility and membrane affinity of EWDP further prolonged residence and time-controlled release feature of Cur in the small intestine. Precursor template-induced multicomponent peptides' self-assembly provides an efficient and controllable strategy for co-enhanced bioactivity and self-assembly capacity of peptides, which could dramatically broaden the functionalization of multicomponent peptides hydrolyzed from natural food proteins.

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

BackgroundCircular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification usually adopt convolution neural network (CNN), recurrent neural network (RNN), or their variants as feature extractors. Most of them have drawbacks such as poor parallelism, insufficient stability, and inability to capture long-term dependencies.MethodsIn this paper, we propose a new method completely using the self-attention mechanism to capture deep semantic features of RNA sequences. On this basis, we construct a CircSSNN model for the cirRNA-RBP identification. The proposed model constructs a feature scheme by fusing circRNA sequence representations with statistical distributions, static local contexts, and dynamic global contexts. With a stable and efficient network architecture, the distance between any two positions in a sequence is reduced to a constant, so CircSSNN can quickly capture the long-term dependencies and extract the deep semantic features.ResultsExperiments on 37 circRNA datasets show that the proposed model has overall advantages in stability, parallelism, and prediction performance. Keeping the network structure and hyperparameters unchanged, we directly apply the CircSSNN to linRNA datasets. The favorable results show that CircSSNN can be transformed simply and efficiently without task-oriented tuning.ConclusionsIn conclusion, CircSSNN can serve as an appealing circRNA-RBP identification tool with good identification performance, excellent scalability, and wide application scope without the need for task-oriented fine-tuning of parameters, which is expected to reduce the professional threshold required for hyperparameter tuning in bioinformatics analysis.

PVA/sodium alginate multi-network aerogel fibers, incorporated with PEG and ZnO, exhibit enhanced temperature regulation, antibacterial, thermal conductivity, and thermal stability

This paper presents a novel approach for the fabrication of polyvinyl alcohol (PVA)/sodium alginate (SA) aerogel fibers with a multilayered network structure using wet spinning and freeze-thaw cycling techniques. The multiple cross-linking networks regulate the pore structure, leading to the formation of stable and tunable multilevel pore architectures. PEG and nano-ZnO were successfully loaded onto the PVA/SA modified aerogel fibers (MAFs) using vacuum impregnation. MAFs exhibited excellent thermal stability at 70 °C without leakage after 24 h of heating. Furthermore, MAFs demonstrated excellent temperature regulation performance, with a latent heat of 121.4 J/g, which accounts for approximately 83 % of PEG. After modification, the thermal conductivity of MAFs was significantly improved, and they exhibited excellent antibacterial properties. Therefore, MAFs are expected to be widely used in intelligent temperature-regulating textiles.

Tunable and stable single-frequency fiber laser by applying Rayleigh feedback light and saturable absorber filter

In this study, a stable and tunable erbium-doped fiber (EDF) ring laser architecture is demonstrated. To filter the dense longitudinal-mode noises and achieve the single-longitudinal-mode (SLM) performance, the Rayleigh backscattering (RB) feedback injection and unpumped EDF saturable absorber (SA) induced filter is applied inside the ring cavity. Here, an effective wavelength-selection scale of the EDF laser is achieved between 1528.0 and 1576.0 nm, when C-band EDF based gain-medium is utilized. Additionally, the wavelength linewidth, optical signal to noise ratio (OSNR), output power and output stability of the fiber laser are also discussed over the available wavelength range in the measurement.

3D porous carbon-embedded nZVI@Fe2O3 nanoarchitectures enable prominent performance and recyclability in antibiotic removal

Overcoming the instability and poor recyclability during the practical applications of contaminant scavengers is a challenging topic. Herein, a three-dimensional (3D) interconnected carbon aerogel (nZVI@Fe2O3/PC) embedding a core-shell nanostructure of nZVI@Fe2O3 was elaborately designed and fabricated via an in-situ self-assembly process. The porous carbon with 3D network architecture exhibits strong adsorption towards various antibiotic contaminants in water, where the stably embedded nZVI@Fe2O3 nanoparticles not only serve as magnetic seeds for recycling, but also avoid the shedding and oxidation of nZVI in the adsorption process. As a result, nZVI@Fe2O3/PC efficiently captures sulfamethoxazole (SMX), sulfamethazine (SMZ), ciprofloxacin (CIP), tetracycline (TC) and other antibiotics in water. In particular, an excellent adsorptive removal capacity of 329 mg g−1 and a rapid capture kinetics (99% of removal efficiency in 10 min) under a wide pH adaptability (2–8) are achieved using nZVI@Fe2O3/PC as an SMX scavenger. nZVI@Fe2O3/PC displays exceptional long-term stability given that it shows excellent magnetic property after it is stored in water solution for 60 d, making it an ideal stable scavenger for contaminants in an etching-resistant and efficient manner. This work would also provide a general strategy to develop other stable iron-based functional architectures for efficient catalytic degradation, energy conversion and biomedicine.

Designing Viscoelastic Gelatin-PEG Macroporous Hybrid Hydrogel with Anisotropic Morphology and Mechanical Properties for Tissue Engineering Application

The mechanical properties of scaffolds play a vital role in regulating key cellular processes in tissue development and regeneration in the field of tissue engineering. Recently, scaffolding material design strategies leverage viscoelasticity to guide stem cells toward specific tissue regeneration. Herein, we designed and developed a viscoelastic Gel-PEG hybrid hydrogel with anisotropic morphology and mechanical properties using a gelatin and functionalized PEG (as a crosslinker) under a benign condition for tissue engineering application. The chemical crosslinking/grafting reaction was mainly involved between epoxide groups of PEG and available functional groups of gelatin. FTIR spectra revealed the hybrid nature of Gel-PEG hydrogel. The hybrid hydrogel showed good swelling behavior (water content > 600%), high porosity and pore interconnectivity suitable for tissue engineering application. Simple unidirectional freezing followed by a freeze-drying technique allowed the creation of structurally stable 3D anisotropic macroporous architecture that showed tissue-like elasticity and was capable of withstanding high deformation (50% strain) without being damaged. The tensile and compressive modulus of Gel-PEG hybrid hydrogel were found to be 0.863 MPa and 0.330 MPa, respectively, which are within the range of normal human articular cartilage. In-depth mechanical characterizations showed that the Gel-PEG hybrid hydrogel possessed natural-tissue-like mechanics such as non-linear and J-shaped stress-strain curves, stress softening effect, high fatigue resistance and stress relaxation response. A month-long hydrolytic degradation test revealed that the hydrogel gradually degraded in a homogeneous manner over time but maintained its structural stability and anisotropic mechanics. Overall, all these interesting features provide a potential opportunity for Gel-PEG hybrid hydrogel as a scaffold in a wide range of tissue engineering applications.

Overworld: Assessing the Geometry of the World for Human-Robot Interaction

For a robot to interact with humans in a given environment, a key need is to understand its environment in terms of the objects composing it, the other agents acting in it, and the relations between all of them. This capability is often called the geometrical situation assessment and is mainly related to spatial reasoning in time. In this letter, we present Overworld, a novel lightweight and open-source framework, merging the key features of a decade of research in the domain. It permanently maintains a geometric state of the world from the point of view of the robot by aggregating perceptual information from several sources and reasoning on them to create a coherent world. Furthermore, Overworld implements perspective-taking by emulating the humans' ability to perceive to estimate the state of the world from their perspective. Finally, thanks to a strong link with an ontology framework, it ensures knowledge coherence in the whole robotic architecture. This work is part of a broader effort to develop a complete, stable, and shareable decisional robotic architecture for Human-Robot Interaction.

The Role of Weak C–H···X (X = O, π) Interactions in Three 1-Hydroxy-2-naphthoic Acid Cocrystals with N-Containing Heteroaromatics: Structural Characterization and Synthon Cooperation

Herein, three novel cocrystals of 1-hydroxy-2-naphthoic acid: tetramethylpyrazine, 1-hydroxy-2-naphthoic acid:1,10-phenanthroline, and 1-hydroxy-2-naphthoic acid:1,4-bis(imidazol-1-ylmethyl)benzene (L2) were obtained by crystallization in methanol–water mixed solvent via a slow evaporation method. The cocrystalline products 1−3 were carried out by a range of techniques, including single-crystal X-ray diffraction, Fourier transform–infrared spectroscopy, elemental analysis, and thermogravimetric testing. We analyzed the crystal structures of the cocrystals 1−3 and found that weak interactions C–H···X (X = O or π) were of great importance in the process of self-assembly as well as strong and conventional hydrogen bonds (N–H···O, O–H···N, O–H···O), leading to a stable and diverse multidimensional supramolecular architecture. It is worth noting that a series of ring motifs with different sizes were explored in the crystal structures of the above complexes, such as R22(5), R22(7), R22(8), R23(13), R24(16), R44(16), R44(22), and so on. The classical and robust supramolecular synthon intermolecular bond between acid and pyridine (acid···pyridine) heterosynthon R22(7), commonly found in organic solids containing carboxylic acids with other N-containing heteroaromatics, was further demonstrated to be involved in the construction of the hydrogen-bond networks of cocrystal 1. The thermogravimetric technique used in this study proved that the mass losses of these three cocrystals were closely related to the strength of the hydrogen bonds in the package fraction.

Predicting seismic interaction effect between soil and structure group using convolutional neural network

Quantifying the influence of seismic interaction between soil and structure group (SSGI) is of great significance to seismic design, retrofit, and damage assessment of structures in densely built urban areas. To this end, this study proposes a one-dimensional convolutional neural network (1D-CNN) model to rapidly predict the influence of adjacent structures on the maximum inter-story drifts and base shears of RC frame structures. Based on an experimentally validated three-dimensional finite element method, 890 pairs of soil-single structure versus soil-structure group systems under different earthquake loadings are simulated. The dataset comprising 890 groups of input (i.e., soil and structure group parameters, ground motion acceleration) and output data (i.e., changes in maximum inter-story drift and base shear) is constructed to train the machine learning model. Subsequently, sensitivity analysis is performed to identify optimal hyperparameters for training the 1D-CNN model, whereas a back propagation artificial neural network (BP-ANN) model is established to compare the model performance. Results indicate that compared with the BP-ANN model, the 1D-CNN model has a more stable and robust architecture and features superior prediction accuracy. In particular, the developed 1D-CNN model has a mean absolute error of less than 2.3% and an absolute error of less than 5.4% for 90% of cases in the testing set. The superior performance of the 1D-CNN model makes it an effective and efficient tool to be applied to predict the seismic responses of RC frame buildings under the SSGI effect.

Three-dimensional nanowire networks fabricated by ion track nanotechnology and their applications

The existing and future accelerator facilities at GSI and FAIR offer unique opportunities for interdisciplinary research, especially for material science and nanotechnology. On their way through polymers, swift heavy ions with GeV energy deposit enormous energy densities along their trajectory, generating long nanoscopic damage trails known as ion tracks. Ion-track technology utilizes the small track size (few nm) combined with the extensive track length (up to 100 μm and more) to synthesize and control the geometry of high-aspect-ratio nanostructures such as tailored nanochannels and nanowires. In particular, electrodeposition and ion-track nanotechnology provide an excellent platform for developing unique 3D networks of nanowires with controlled dimensions, composition and crystallographic properties. Here, a summary of recent results obtained on the synthesis and characterization of stable 3D architectures of semiconductor and semimetal nanowires, and their implementation in the fields of photoelectrochemistry and thermoelectrics, is presented.

One-step growth of the interconnected carbon nanotubes/graphene hybrids from cuttlebone-derived bi-functional catalyst for lithium-ion batteries

Carbon nanotubes/graphene hybrid materials with excellent physicochemical properties can be widely applied in the fields of energy storage, electrocatalysis, sensing, etc. Reducing the self-stacking and achieving covalent interaction between carbon nanotubes and graphene are important to ensure a stable hierarchical architecture and effective mass transfer. Herein, we propose a one-step strategy to synthesize 3D interconnected carbon nanotubes/graphene hybrids on the easy-to-remove biomass-derived substrate. The calcined natural cuttlebone as bi-functional catalyst precursor can simultaneously grow carbon nanotubes and graphene by one-step chemical vapor deposition without the addition of extra metal catalysts, while the interconnected structure can act as the porous template for graphene growth. The simultaneous growth process can obtain covalent bonding between carbon nanotubes and graphene, while the crystalline quality and interlayer space can be adjusted by different carbon sources and growth parameters (e.g., temperature). The one-step grown carbon nanotubes/graphene hybrids with seamless interfaces and hierarchical interconnected 3D structure can effectively enhance the electron transfer as well as the electrolyte infiltration efficiency. When utilized as lithium-ion batteries (LIBs) anode, a high specific capacity (544 mAh g–1 at 0.1 A g–1), good rate capability (200 mAh g–1 at 6.4 A g–1 with an ultrashort charge time of 113 s), and excellent cyclic stability can be achieved. This simple and one-step carbon nanotubes/graphene hybrids fabrication strategy can be easily scale-up and applied in various fields.

Degeneracy and stability in neural circuits of dopamine and serotonin neuromodulators: A theoretical consideration.

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modeling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modeling is constrained by relevant experimental studies of the VTA or DRN system using, e.g., electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed, and mixed DRNVTA neuronal signaling in reward and punishment tasks. The inability of this model to recapitulate all observed neuronal signaling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate the D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations' activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implications for the stable and robust maintenance of neuromodulatory functions.

Optimisation of Automation Devices Based on IOT Big Data Algorithms

With the continuous development of communication, sensor and caching technologies, IoT technology has gained rapid opportunities for growth and a huge digital revolution has taken place at all levels of society. Blockchain technology has emerged rapidly in recent years and can be seen as a distributed, time-based ledger of distributed data. It utilises technologies such as consensus protocols, modern cryptography, P2P and smart contracts, which can provide a secure, stable, transparent, auditable and low-consumption system architecture that has a traceable, stable and efficient security management capability, and can provide a new solution to identity security for the Internet of Things. This paper absorbs the existing blockchain-based access management approach and improves it, proposing a new private chain-based security management approach that solves the problems of access dynamics, low intelligence and high overhead in the traditional access management approach. This paper designs a new control management architecture, the Novel-Capability-Based Access Control (NCBAC), which draws on the microkernel and microservice ideas of operating systems. Firstly, this paper abstracts the concept of management node to solve the problem of weak computing power and low storage performance of IoT devices that cannot meet the difficulty of direct communication between IoT devices and blockchain, and at the same time can reduce the network operation overhead; secondly, it constructs a multi-level smart contract system and designs three kinds of smart contracts, AC, ACC and AMC, to build a trusted and reliable access control entity model; finally, it adopts radial basis based (RBF) neural network and combines with access policy to dynamically generate the credit degree threshold of access nodes to build an intelligent access authority management model for IoT mass sensors. The model proposed in this paper designs a token mechanism based on the fact that IoT systems have multiple requests within a short period of time in a real production environment, which, according to experimental results, improves the performance of the system to a certain extent.

A dibutylhydroquinone/dibutylbenzoquinone-Cd2+/Cd self-stratified Battery

Self-stratified battery is a new type of rechargeable battery potentially applicable for large-scale energy storage. It has a thermodynamically stable membrane-free self-stratified architecture which endows the battery with low cost, high cycling stability and excellent safety. In this paper, a novel self-stratified battery based on quinone cathode is reported. 2,5-di-tert-butyl-1,4-hydroquinone (DBHQ) is studied as the reversible redox active material at the cathode side. It is dissolved in an organic solvent to form the catholyte which is immiscible with the aqueous electrolyte. The aqueous electrolyte is composed of sulfuric acid/magnesium sulfate/cadmium sulfate mixed aqueous solution. Cadmium metal is used as the anode material. Due to density difference, the organic catholyte, aqueous electrolyte and cadmium metal anode are automatically stratified and forms a thermodynamically stable cell structure. The charge/discharge process of the battery is accompanied with the migration of proton between the organic and aqueous phases. Interestingly, we found that adding bis trifluoromethyl sulfonimide anion (TFSI − ) in the organic phase would effectively reduce the battery polarization and improve the cycle performance. Through spectroscopic analysis, we ascribe such performance improvement to the increase of proton concentration in the organic layer and the solvation structure change of DBHQ molecules. Our work extends the active materials system of self-stratified cells to quinone compounds and demonstrates novel performance influencing mechanism based on catholyte solvation structure.

Porous-tube arrays with electron-correlated strong synergy of sulfide/ phosphide heterostructures for ultrahigh-capacity and stable battery

This study reports a significant advance toward the multicomponent design and fabrication of novel hierarchical porous-tube array electrodes by engineering surfaces and interfaces of different active materials. The hierarchical electrodes with an electron-correlated strong synergy of traditional bimetallic sulfide/phosphide heterostructures were achieved by controlled ion-exchange processes. Each submicron-sized tube has a porous shell consisting of Fe7(PO4)6 inner layer with metalloid-like electrical conductivity wrapped by interlaced ultrathin CoS nanoflakes, which provides both a stable nano-micron architecture and highly active heterostructure interfaces. The CoS/Fe7(PO4)6 heterostructure electrode showed an ultrahigh specific capacity reaching up to 461.5mAh·g−1 at 5 mA·cm−2, much higher than that of a single component and previously reported multicomponent ones. Particularly, the electrode also showed remarkable stability with capacity retention of 92.0% after 10,000 charge-discharge cycles at a high current density of 30 mA·cm−2. The practical application demonstrated that two connected devices in a series can power 16 LEDs. This work provides an effective pathway to hierarchical heterostructure arrays for energy storage devices with high energy density, fast recharging ability, and long cycle life.