Showing papers by "City University of Hong Kong published in 2018"

Non-fullerene acceptors for organic solar cells

Abstract: Non-fullerene acceptors (NFAs) are currently a major focus of research in the development of bulk-heterojunction organic solar cells (OSCs). In contrast to the widely used fullerene acceptors (FAs), the optical properties and electronic energy levels of NFAs can be readily tuned. NFA-based OSCs can also achieve greater thermal stability and photochemical stability, as well as longer device lifetimes, than their FA-based counterparts. Historically, the performance of NFA OSCs has lagged behind that of fullerene devices. However, recent developments have led to a rapid increase in power conversion efficiencies for NFA OSCs, with values now exceeding 13%, demonstrating the viability of using NFAs to replace FAs in next-generation high-performance OSCs. This Review discusses the important work that has led to this remarkable progress, focusing on the two most promising NFA classes to date: rylene diimide-based materials and materials based on fused aromatic cores with strong electron-accepting end groups. The key structure–property relationships, donor–acceptor matching criteria and aspects of device physics are discussed. Finally, we consider the remaining challenges and promising future directions for the NFA OSCs field. Non-fullerene acceptors have been widely used in organic solar cells over the past 3 years. This Review focuses on the two most promising classes of non-fullerene acceptors — rylene diimide-based materials and fused-ring electron acceptors — and discusses structure–property relationships, donor– acceptor matching criteria and device physics, as well as future research directions for the field.

Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages

Abstract: Electro-optic modulators translate high-speed electronic signals into the optical domain and are critical components in modern telecommunication networks1,2 and microwave-photonic systems3,4. They are also expected to be building blocks for emerging applications such as quantum photonics5,6 and non-reciprocal optics7,8. All of these applications require chip-scale electro-optic modulators that operate at voltages compatible with complementary metal–oxide–semiconductor (CMOS) technology, have ultra-high electro-optic bandwidths and feature very low optical losses. Integrated modulator platforms based on materials such as silicon, indium phosphide or polymers have not yet been able to meet these requirements simultaneously because of the intrinsic limitations of the materials used. On the other hand, lithium niobate electro-optic modulators, the workhorse of the optoelectronic industry for decades9, have been challenging to integrate on-chip because of difficulties in microstructuring lithium niobate. The current generation of lithium niobate modulators are bulky, expensive, limited in bandwidth and require high drive voltages, and thus are unable to reach the full potential of the material. Here we overcome these limitations and demonstrate monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels. We achieve this by engineering the microwave and photonic circuits to achieve high electro-optical efficiencies, ultra-low optical losses and group-velocity matching simultaneously. Our scalable modulator devices could provide cost-effective, low-power and ultra-high-speed solutions for next-generation optical communication networks and microwave photonic systems. Furthermore, our approach could lead to large-scale ultra-low-loss photonic circuits that are reconfigurable on a picosecond timescale, enabling a wide range of quantum and classical applications5,10,11 including feed-forward photonic quantum computation. Chip-scale lithium niobate electro-optic modulators that rapidly convert electrical to optical signals and use CMOS-compatible voltages could prove useful in optical communication networks, microwave photonic systems and photonic computation.

Domain Generalization with Adversarial Feature Learning

Abstract: In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.

Multicomponent intermetallic nanoparticles and superb mechanical behaviors of complex alloys

Abstract: Alloy design based on single-principal-element systems has approached its limit for performance enhancements. A substantial increase in strength up to gigapascal levels typically causes the premature failure of materials with reduced ductility. Here, we report a strategy to break this trade-off by controllably introducing high-density ductile multicomponent intermetallic nanoparticles (MCINPs) in complex alloy systems. Distinct from the intermetallic-induced embrittlement under conventional wisdom, such MCINP-strengthened alloys exhibit superior strengths of 1.5 gigapascals and ductility as high as 50% in tension at ambient temperature. The plastic instability, a major concern for high-strength materials, can be completely eliminated by generating a distinctive multistage work-hardening behavior, resulting from pronounced dislocation activities and deformation-induced microbands. This MCINP strategy offers a paradigm to develop next-generation materials for structural applications.

Climate change and interconnected risks to sustainable development in the Mediterranean

Abstract: Recent accelerated climate change has exacerbated existing environmental problems in the Mediterranean Basin that are caused by the combination of changes in land use, increasing pollution and declining biodiversity. For five broad and interconnected impact domains (water, ecosystems, food, health and security), current change and future scenarios consistently point to significant and increasing risks during the coming decades. Policies for the sustainable development of Mediterranean countries need to mitigate these risks and consider adaptation options, but currently lack adequate information — particularly for the most vulnerable southern Mediterranean societies, where fewer systematic observations schemes and impact models are based. A dedicated effort to synthesize existing scientific knowledge across disciplines is underway and aims to provide a better understanding of the combined risks posed.

An extremely safe and wearable solid-state zinc ion battery based on a hierarchical structured polymer electrolyte

Abstract: Flexible and safe batteries, coupled with high performance and low cost, constitute a radical advance in portable and wearable electronics, especially considering the fact that these flexible devices are likely to experience more mechanical impacts and potential damage than well-protected rigid batteries. However, flexible lithium ion batteries (LIBs) are vastly limited by their intrinsic safety and cost issues. Here we introduce an extremely safe and wearable solid-state zinc ion battery (ZIB) comprising a novel gelatin and PAM based hierarchical polymer electrolyte (HPE) and an α-MnO2 nanorod/carbon nanotube (CNT) cathode. Benefiting from the well-designed electrolyte and electrodes, the flexible solid-state ZIB delivers a high areal energy density and power density (6.18 mW h cm−2 and 148.2 mW cm−2, respectively), high specific capacity (306 mA h g−1) and excellent cycling stability (97% capacity retention after 1000 cycles at 2772 mA g−1). More importantly, the solid-state ZIB offers a high wearability and an extreme safety performance over conventional flexible LIBs, and performs very well under various severe conditions, such as being greatly cut, bent, hammered, punctured, sewed, washed in water or even put on fire. In addition, flexible ZIBs were integrated in series to power a commercial smart watch, a wearable pulse sensor, and a smart insole, which has been achieved to the best of our knowledge for the first time. These results demonstrate the promising potential of ZIBs in many practical wearable applications and offer a new platform for flexible and wearable energy storage technologies.

Gated Fusion Network for Single Image Dehazing

Abstract: In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms.

VITAL: VIsual Tracking via Adversarial Learning

Abstract: The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as background in the second stage. The performance of existing trackers using deep classification networks is limited by two aspects. First, the positive samples in each frame are highly spatially overlapped, and they fail to capture rich appearance variations. Second, there exists extreme class imbalance between positive and negative samples. This paper presents the VITAL algorithm to address these two problems via adversarial learning. To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes. With the use of adversarial learning, our network identifies the mask that maintains the most robust features of the target objects over a long temporal span. In addition, to handle the issue of class imbalance, we propose a high-order cost sensitive loss to decrease the effect of easy negative samples to facilitate training the classification network. Extensive experiments on benchmark datasets demonstrate that the proposed tracker performs favorably against state-of-the-art approaches.

ContractFuzzer: fuzzing smart contracts for vulnerability detection

Abstract: Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts Millions of smart contracts have been deployed in various decentralized applications The security vulnerabilities within those smart contracts pose significant threats to their applications Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to USD 60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of USD 30 million and the freezing of USD 150 million worth of Ether

Approaching the capacity limit of lithium cobalt oxide in lithium ion batteries via lanthanum and aluminium doping

Abstract: Lithium cobalt oxides (LiCoO2) possess a high theoretical specific capacity of 274 mAh g–1. However, cycling LiCoO2-based batteries to voltages greater than 4.35 V versus Li/Li+ causes significant structural instability and severe capacity fade. Consequently, commercial LiCoO2 exhibits a maximum capacity of only ~165 mAh g–1. Here, we develop a doping technique to tackle this long-standing issue of instability and thus increase the capacity of LiCoO2. La and Al are concurrently doped into Co-containing precursors, followed by high-temperature calcination with lithium carbonate. The dopants are found to reside in the crystal lattice of LiCoO2, where La works as a pillar to increase the c axis distance and Al as a positively charged centre, facilitating Li+ diffusion, stabilizing the structure and suppressing the phase transition during cycling, even at a high cut-off voltage of 4.5 V. This doped LiCoO2 displays an exceptionally high capacity of 190 mAh g–1, cyclability with 96% capacity retention over 50 cycles and significantly enhanced rate capability. Lithium cobalt oxides are used as a cathode material in batteries for mobile devices, but their high theoretical capacity has not yet been realized. Here, the authors present a doping method to enhance diffusion of Li ions as well as to stabilize structures during cycling, leading to impressive electrochemical performance.

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Abstract: Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca.

Waterproof and Tailorable Elastic Rechargeable Yarn Zinc Ion Batteries by a Cross-Linked Polyacrylamide Electrolyte

Abstract: Emerging research toward next-generation flexible and wearable electronics has stimulated the efforts to build highly wearable, durable, and deformable energy devices with excellent electrochemical performances. Here, we develop a high-performance, waterproof, tailorable, and stretchable yarn zinc ion battery (ZIB) using double-helix yarn electrodes and a cross-linked polyacrylamide (PAM) electrolyte. Due to the high ionic conductivity of the PAM electrolyte and helix structured electrodes, the yarn ZIB delivers a high specific capacity and volumetric energy density (302.1 mAh g–1 and 53.8 mWh cm–3, respectively) as well as excellent cycling stability (98.5% capacity retention after 500 cycles). More importantly, the quasi-solid-state yarn ZIB also demonstrates superior knittability, good stretchability (up to 300% strain), and superior waterproof capability (high capacity retention of 96.5% after 12 h underwater operation). In addition, the long yarn ZIB can be tailored into short ones, and each part sti...

Initiating a mild aqueous electrolyte Co3O4/Zn battery with 2.2 V-high voltage and 5000-cycle lifespan by a Co(III) rich-electrode

Abstract: The Zn/Co3O4 battery is one of the few aqueous electrolyte batteries with a potential >2 V voltage. Unfortunately, so far, all reported Zn/Co3O4 batteries are using an alkaline electrolyte, resulting in poor cycling stability and environmental problems. Here, we report a Co(III) rich-Co3O4 nanorod material with vastly improved electrochemical kinetics. Zn/Co(III) rich-Co3O4 batteries can work well in ZnSO4 with a CoSO4 additive aqueous solution as a mild electrolyte, delivering a high voltage of 2.2 V, a capacity of 205 mA h g−1 (Co3O4) and an extreme cycling stability of 92% capacity retention even after 5000 cycles. Further mechanistic study reveals a conversion reaction between in situ formed CoO and Co3O4, which has never been observed in an alkaline Zn/Co3O4 battery. Subsequently, a flexible solid-state battery is constructed and reveals high flexibility and a high energy density of 360.8 W h kg−1 at a current density of 0.5 A g−1. Our research initiates the first Zn/Co3O4 battery working in a mild electrolyte, resulting in excellent electrochemical performance. It also indicates that the electrochemical kinetics can be effectively enhanced by fine tuning the atomic structure of electrode materials, opening a new door to improve the performance of aqueous electrolyte batteries.

Near-Infrared Excitation/Emission and Multiphoton-Induced Fluorescence of Carbon Dots.

Abstract: Carbon dots (CDs) have significant potential for use in various fields including biomedicine, bioimaging, and optoelectronics. However, inefficient excitation and emission of CDs in both near-infrared (NIR-I and NIR-II) windows remains an issue. Solving this problem would yield significant improvement in the tissue-penetration depth for in vivo bioimaging with CDs. Here, an NIR absorption band and enhanced NIR fluorescence are both realized through the surface engineering of CDs, exploiting electron-acceptor groups, namely molecules or polymers rich in sulfoxide/carbonyl groups. These groups, which are bound to the outer layers and the edges of the CDs, influence the optical bandgap and promote electron transitions under NIR excitation. NIR-imaging information encryption and in vivo NIR fluorescence imaging of the stomach of a living mouse using CDs modified with poly(vinylpyrrolidone) in aqueous solution are demonstrated. In addition, excitation by a 1400 nm femtosecond laser yields simultaneous two-photon-induced NIR emission and three-photon-induced red emission of CDs in dimethyl sulfoxide. This study represents the realization of both NIR-I excitation and emission as well as two-photon- and three-photon-induced fluorescence of CDs excited in an NIR-II window, and provides a rational design approach for construction and clinical applications of CD-based NIR imaging agents.

Efficient RNA drug delivery using red blood cell extracellular vesicles

Abstract: Most of the current methods for programmable RNA drug therapies are unsuitable for the clinic due to low uptake efficiency and high cytotoxicity. Extracellular vesicles (EVs) could solve these problems because they represent a natural mode of intercellular communication. However, current cellular sources for EV production are limited in availability and safety in terms of horizontal gene transfer. One potentially ideal source could be human red blood cells (RBCs). Group O-RBCs can be used as universal donors for large-scale EV production since they are readily available in blood banks and they are devoid of DNA. Here, we describe and validate a new strategy to generate large-scale amounts of RBC-derived EVs for the delivery of RNA drugs, including antisense oligonucleotides, Cas9 mRNA, and guide RNAs. RNA drug delivery with RBCEVs shows highly robust microRNA inhibition and CRISPR–Cas9 genome editing in both human cells and xenograft mouse models, with no observable cytotoxicity.

Carbon emission of global construction sector

Abstract: The construction sector delivers the infrastructure and buildings to the society by consumption large amount of unrenewable energy. Consequently, this consumption causes the large emission of CO2. This paper explores and compares the level of CO2 emission caused by the construction activities globally by using the world environmental input-output table 2009. It analyses CO2 emission of construction sector in 40 countries, considering 26 kinds of energy use and non-energy use. Results indicate: 1) the total CO2 emission of the global construction sector was 5.7 billion tons in 2009, contributing 23% of the total CO2 emissions produced by the global economics activities. 94% of the total CO2 from the global construction sector are indirect emission. 2) Gasoline, diesel, other petroleum products and light fuel oil are four main energy sources for direct CO2 emission of global construction sector. The indirect CO2 emission mainly stems from hard coal, nature gas, and non-energy use. 3) The emerging economies cause nearly 60% of the global construction sector total CO2 emission. China is the largest contributor. Moreover, the intensities of construction sector’s direct and indirect CO2 emission in the developing countries are larger than the value in the developed countries. Therefore, promoting the development and use of the low embodied carbon building material and services, the energy efficiency of construction machines, as well as the renewable energy use are identified as three main pivotal opportunities to reduce the carbon emissions of the construction sector.

A bioinspired multilegged soft millirobot that functions in both dry and wet conditions

Abstract: Developing untethered millirobots that can adapt to harsh environments with high locomotion efficiency is of interest for emerging applications in various industrial and biomedical settings. Despite recent success in exploiting soft materials to impart sophisticated functions which are not available in conventional rigid robotics, it remains challenging to achieve superior performances in both wet and dry conditions. Inspired by the flexible, soft, and elastic leg/foot structures of many living organisms, here we report an untethered soft millirobot decorated with multiple tapered soft feet architecture. Such robot design yields superior adaptivity to various harsh environments with ultrafast locomotion speed (>40 limb length/s), ultra-strong carrying capacity (>100 own weight), and excellent obstacle-crossing ability (stand up 90° and across obstacle >10 body height). Our work represents an important advance in the emerging area of bio-inspired robotics and will find a wide spectrum of applications.

Dithienopicenocarbazole-Based Acceptors for Efficient Organic Solar Cells with Optoelectronic Response Over 1000 nm and an Extremely Low Energy Loss.

Abstract: Two cheliform non-fullerene acceptors, DTPC-IC and DTPC-DFIC, based on a highly electron-rich core, dithienopicenocarbazole (DTPC), are synthesized, showing ultra-narrow bandgaps (as low as 1.21 eV). The two-dimensional nitrogen-containing conjugated DTPC possesses strong electron-donating capability, which induces intense intramolecular charge transfer and intermolecular π–π stacking in derived acceptors. The solar cell based on DTPC-DFIC and a spectrally complementary polymer donor, PTB7-Th, showed a high power conversion efficiency of 10.21% and an extremely low energy loss of 0.45 eV, which is the lowest among reported efficient OSCs.

Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks

Abstract: Due to the spatially variant blur caused by camera shake and object motions under different scene depths, deblurring images captured from dynamic scenes is challenging. Although recent works based on deep neural networks have shown great progress on this problem, their models are usually large and computationally expensive. In this paper, we propose a novel spatially variant neural network to address the problem. The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). RNN is used as a deconvolution operator performed on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the weights for the RNN at every location. As a result, the RNN is spatially variant and could implicitly model the deblurring process with spatially variant kernels. The third CNN is used to reconstruct the final deblurred feature maps into restored image. The whole network is end-to-end trainable. Our analysis shows that the proposed network has a large receptive field even with a small model size. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size.

A Generic Deep-Learning-Based Approach for Automated Surface Inspection

Abstract: Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%–25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%–19.00% in three defect types and improves accuracies by 2.29%–9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

Abstract: Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.

Hyperspectral Image Classification With Deep Learning Models

Abstract: Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Single-Site Active Iron-Based Bifunctional Oxygen Catalyst for a Compressible and Rechargeable Zinc-Air Battery.

Abstract: The exploitation of a high-efficient, low-cost, and stable non-noble-metal-based catalyst with oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) simultaneously, as air electrode material for a rechargeable zinc–air battery is significantly crucial Meanwhile, the compressible flexibility of a battery is the prerequisite of wearable or/and portable electronics Herein, we present a strategy via single-site dispersion of an Fe–Nx species on a two-dimensional (2D) highly graphitic porous nitrogen-doped carbon layer to implement superior catalytic activity toward ORR/OER (with a half-wave potential of 086 V for ORR and an overpotential of 390 mV at 10 mA·cm–2 for OER) in an alkaline medium Furthermore, an elastic polyacrylamide hydrogel based electrolyte with the capability to retain great elasticity even under a highly corrosive alkaline environment is utilized to develop a solid-state compressible and rechargeable zinc–air battery The creatively developed battery has a low charge–discha