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Abstract Electrochemical hydrogen peroxide (H2O2) production (EHPP) via a two-electron oxygen reduction reaction (2e- ORR) provides a promising alternative to replace the energy-intensive anthraquinone process. M-N-C electrocatalysts, which consist of atomically dispersed transition metals and nitrogen-doped carbon, have demonstrated considerable EHPP efficiency. However, their full potential, particularly regarding the correlation between structural configurations and performances in neutral media, remains underexplored. Herein, a series of ultralow metal-loading M-N-C electrocatalysts are synthesized and investigated for the EHPP process in the neutral electrolyte. CoNCB material with the asymmetric Co-C/N/O configuration exhibits the highest EHPP activity and selectivity among various as-prepared M-N-C electrocatalyst, with an outstanding mass activity (6.1 × 105 A gCo −1 at 0.5 V vs. RHE), and a high practical H2O2 production rate (4.72 mol gcatalyst −1 h−1 cm−2). Compared with the popularly recognized square-planar symmetric Co-N4 configuration, the superiority of asymmetric Co-C/N/O configurations is elucidated by X-ray absorption fine structure spectroscopy analysis and computational studies.

In order to prevent structural damage or high repair costs caused by concrete crack propagation, the use of microbial-induced CaCO3 precipitation to repair concrete cracks has been a hot topic in recent years. However, due to environmental constraints such as oxygen concentration, the width and depth of repaired cracks are seriously insufficient, which affects the further development of microbial self-healing agents. In this paper, a ternary microbial self-healing agent composed of different proportions of Bacillus pasteurii, Saccharomyces cerevisiae, and Bacillus mucilaginosus was designed, and its crack repair ability was evaluated. When the mixing ratio was 7:1:2, the cell concentration was the highest, the precipitation amount of CaCO3 was the highest, and the crystallinity of calcite crystal was the highest. Compared to the single microorganism, the mortar specimens with ternary microorganisms had the largest repair area (up to 100%) and the deepest repair depth (CaCO3 presents at 9–12 mm from the crack surface). This is because when the concrete breaks, all three microorganisms are activated by water, O2, and CO2. Saccharomyces cerevisiae and Bacillus mucilaginosus accelerated the growth of Bacillus pasteurii and more mineralized products; CaCO3 was rapidly formed and quickly filled on the crack surface. When CaCO3 seals the surface of the crack, the internal Saccharomyces cerevisiae and Bacillus mucilaginosus continue to play a role. Bacillus mucilaginosus can accelerate the dissolution of CO2 produced by the anaerobic fermentation of Saccharomyces cerevisiae and the hydrolysis of CO32−, thereby improving the repair of the crack depth direction.

Lightweight geopolymers have the advantages of a wide source of raw materials, chemical corrosion resistance, high mechanical strength and excellent durability, and are expected to replace traditional building insulation materials. In this paper, a green bio-based foaming agent with a small 1 h settlement distance, high average foaming multiple and low bleeding ratio was obtained by a Cetyltrimethylammonium Bromide/yeast solution. When the amount of Cetyltrimethylammonium Bromide is 0.50 wt%, the foam prepared by the yeast and Cetyltrimethylammonium Bromide solution exhibits the improved 1 h settlement distance, the large average foaming multiple, the small bleeding ratio and uniform foam size. Subsequently, a lightweight geopolymer based on metakaolin and fly ash (or silica fume) was successfully prepared by the bio-based foaming agent, and the effects of different foam content on the properties of the geopolymer, such as dry density, water absorption, thermal conductivity, compressive strength and morphology, were studied. With an increase in foam content, the dry density, thermal conductivity and compressive strength of the geopolymer gradually decrease, the water absorption increases, regardless of whether silica fume or fly ash are added. Herein, it is confirmed that the foaming agent based on yeast can be effectively used to prepare lightweight geopolymers, which can provide vast opportunities to turn into candidates for novel inorganic thermal insulation materials.

Water damage is one of the major distresses of asphalt pavements. Existing methods for investigating the water stability of asphalt mixtures rely primarily on static water test methods, the tensile strength ratio (TSR) test, and the retained Marshall stability (RMS) test, which evaluate the strength and stability loss after freeze-thaw damage or hot water immersion, respectively. However, these methods do not accurately replicate the actual dynamic water damage conditions to pavement. Therefore, in this study, a variety of damage conditions, including static water conditions and dynamic water pressure conditions, were used to investigate the effects of lignin fibers (LFs), polyester fibers (PFs), and polypropylene fibers (PPFs) on the water stability of asphalt mixtures. First, three fibers-enhanced SMA gap-gradation asphalt mixtures were designed. Then, TSR and RMS were measured under traditional static water damage conditions and new dynamic water pressure damage conditions to evaluate the effect of fiber types on the water stability of asphalt mixtures. Finally, the void rate of asphalt mixtures and its changes under dynamic water damage conditions were further revealed with the help of CT scanning technique. Results showed that, among these three types of fibers, PFs-enhanced asphalt mixture exhibited excellent stability under both static and dynamic water conditions, and the CT scanning test also indicated that the PFs can significantly reduce the increase rate of voids in asphalt mixtures after dynamic water pressure damage. This study identified the potential of incorporating suitable type of fiber to enhance the performance of asphalt mixture under dynamic water pressure damage.

To achieve stabilisation control of an underactuated bridge crane system, a new robust control strategy for the sliding mode is proposed in this paper. It can realise finite-time-convergent stabilisation control under the conditions of model uncertainty, parameter perturbation and external interference. In contrast to the existing methods, our method does not need prior information of the dynamic characteristics of the bridge crane system, and can make the system converge to the equilibrium state at the preset time. Specifically, the nonlinear model of the bridge crane system is linearised with partial feedback, and adaptive signals are introduced. Then, according to the form of the transformed system, a fast terminal sliding mode surface is constructed, and an adaptive terminal sliding mode controller is designed. According to strict analysis, the proposed control law ensures that the system converges to the equilibrium point in finite time and provides the convergence time. Finally, the effectiveness and robustness of the proposed control method are verified by comparing the simulation and experimental results with existing methods.

Hand gestures are a well-known and straightforward method of human-computer interaction. The majority of the study focused on hand gesture recognition. However, little work has been done to develop a complete set of gesture recognition applications. With the improvement of model feature extraction ability and the increase in the number of model parameters, it is becoming more challenging to achieve a small memory footprint on mobile devices based on an ARM architecture or CPU devices based on x86 architecture. However, these existing methods are heavy, requiring more memory and inference time. The execution of memory-efficient CNNs without compromising accuracy has been a challenge, especially when the inference has to be performed on an edge computing device in real time. Therefore, we propose a lightweight network for hand gesture recognition (LHGR-Net) and deploy it on a Raspberry Pi. LHGR-Net consists of three main parts: the base network structure, the multiscale structure (MSS), and the lightweight attention structure (LAS). We present pre-trained weights that are learned from other data to initialize the network structure. In addition, the LHGR-Net model was made to be deployed on a Raspberry Pi, and a deployed model can be used to control home appliances. Extensive experiments show that our method achieves almost as good as state-of-the-art performance in hand gesture recognition and running time.

Abstract The paper presents a novel algorithm to classify children's epileptic syndromes based on the fused features of electroencephalogram (EEG) and electrocardiogram (ECG). The purpose is to assess whether multimodal physiological signals could improve the classification performance of epileptic syndromes over a single physiological signal. The study is carried out on the epileptic syndromes database recorded by the Children's Hospital, Zhejiang University School of Medicine (CHZU), that includes the synchronised EEGs and ECGs of 16 children suffered from the infantile spasms (known as the WEST syndrome, named) and the childhood absence epilepsy (CAE), respectively. Experiments are conducted and compared using the EEGs and ECGs in the ictal and interictal periods. The data imbalanced issue between the ictal and interictal periods is also considered by applying a synthetic minority sample generating approach. The experimental results show that using the fused feature of EEG + ECG can achieve an average of 98.15% overall classification accuracy, which is better than using the single physiological signal.

Human intelligence has the advantage for making high-level decisions in the remote control of underwater vehicles, while autonomous control is superior for accurate and fast close-range pose adjustment. Combining the advantages of both remote and autonomous control, this paper proposes a visual-aided shared-control method for a semi-autonomous underwater vehicle (sAUV) to conduct flexible, efficient and stable underwater grasping. The proposed method utilizes an arbitration mechanism to assign the authority weights of the human command and the automatic controller according to the attraction field (AF) generated by the target objects. The AF intensity is adjusted by understanding the human intention, and the remote-operation command is fused with a visual servo controller. The shared controller is designed based on the kinematic and dynamic models, and model parameter uncertainties are also addressed. Efficient and stable control performance is validated by both simulation and experiment. Faster and accurate dynamic positioning in front of the target object is achieved using the shared-control method. Compared to the pure remote operation mode, the shared-control mode significantly reduces the average time consumption on grasping tasks for both skilled and unskilled operators.

Objectives: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. Methods: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. Results: Experiments on EEGs of 16 subjects from the Children’s Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. Significance: The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

Abstract The extreme learning machine‐based autoencoder (ELM‐AE) has attracted a lot of attention due to its fast learning speed and promising representation capability. However, the existing ELM‐AE algorithms only reconstruct the original input and generally ignore the probability distribution of the data. The minimum error entropy (MEE), as an optimal criterion considering the distribution statistics of the data, is robust in handling non‐linear systems and non‐Gaussian noises. The MEE is equivalent to the minimisation of the Kullback–Leibaler divergence. Inspired by these advantages, a novel randomised AE is proposed by adopting the MEE criterion as the loss function in the ELM‐AE (in short, the MEE‐RAE) in this study. Instead of solving the output weight by the Moore–Penrose generalised inverse, the optimal output weight is obtained by the fixed‐point iteration method. Further, a quantised MEE (QMEE) is applied to reduce the computational complexity of. Simulations have shown that the QMEE‐RAE not only achieves superior generalisation performance but is also more robust to non‐Gaussian noises than the ELM‐AE.

The performance of asphalt binders and asphalt mixtures can be enhanced by the inclusion of fiber. The viscoelastic characteristics of fiber-reinforced asphalt binders and their corresponding mixtures were characterized in this study. To generate fiber-reinforced asphalt samples for dynamic shear rheometer (DSR) tests, polypropylene fibers (PPFs), polyester fibers (PFs), and lignin fibers (LFs) were added into modified asphalt with a ratio of 5wt%. Indirect tensile resilience tests were conducted on the fiber-reinforced asphalt mixture with Marshall samples, which was prepared with a 6.4% of bitumen/aggregate ratio. The addition of fiber can increase the anti-rutting performance of asphalt binders, and also reduce the anti-fatigue performance of asphalt binders to varying degrees. Viscoelastic properties of the fiber-reinforced asphalt binders are highly dependent on the shape of the used fiber. The resistance of the fiber-reinforced asphalt binders to rutting at high temperatures increases with the roughness degree of the fiber’s surface morphology. PPF-reinforced asphalt binders surpass the others in terms of anti-rutting capabilities. The high-temperature deformation resistance of the PPF-reinforced asphalt mixture is stronger, whereas the low-temperature crack resistance of the PF-reinforced asphalt mixture is stronger, which can be observed from the master curve of indirect tensile resilient modulus.

Planar type docking enables a convenient underwater energy supply for irregularly shaped autonomous underwater vehicles (AUVs), but the corresponding control method is still challenging. Conventional control methods for torpedo-shaped AUVs are not suitable for planar type docking due to the significant differences in system structures and motion characteristics. This paper proposes a two-step adaptive control method to solve the planar type docking problem. The method makes a seamless combination of horizontal dynamic positioning and visual servo docking considering ocean current disturbance. The current disturbance is estimated and canceled in the pre-docking step using a current observer, and the positioning error is further compensated for by the vertical visual servo technique in the docking step. Reduced order dynamic models are distinctively established for different docking steps according to the motion characteristics, based on which the dynamic controllers are designed considering the model parameter uncertainties. Simulation is conducted with an initial distance of 10 m in the horizontal direction and 3 m in depth. Stable and accurate dynamic positioning under up to 0.4 m/s of current disturbances with different directions is validated. A 0.5 m lateral positioning error is successfully compensated for by the visual servo docking step. The proposed control method provides a valuable reference for similar types of docking application.

Ion–conducting ceramic membranes, such as mixed oxygen ionic and electronic conducting (MIEC) membranes and mixed proton–electron conducting (MPEC) membranes, have the potential for absolute selectivity for specific gases at high temperatures. By utilizing these membranes in membrane reactors, it is possible to combine reaction and separation processes into one unit, leading to a reduction in by–product formation and enabling the use of thermal effects to achieve efficient and sustainable chemical production. As a result, membrane reactors show great promise in the production of various chemicals and fuels. This paper provides an overview of recent developments in dense ceramic catalytic membrane reactors and their potential for chemical production. This review covers different types of membrane reactors and their principles, advantages, disadvantages, and key issues. The paper also discusses the configuration and design of catalytic membrane reactors. Finally, the paper offers insights into the challenges of scaling up membrane reactors from experimental stages to practical applications.

This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle’s invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC and PDSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn( $S$ ), the designed continuous function th( $S$ ) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.

A wide application of sliding mode variable structure control as a nonlinear robust control method, has been witnessed in anti-swing positioning control of bridge crane system. Aiming at the problem that the sliding mode variable structure control system of bridge crane is not robust in approaching process, a new Global-Equivalent Sliding Mode Controller (GESMC) based on bridge crane system is proposed. This controller can realize the anti-sway positioning control of the bridge crane system under the condition of uncertain model parameters and external disturbance. The proposed controller, different from the traditional sliding mode control, excels in improving system robustness through keeping the system states in the sliding surface during the whole response process. Specifically, it initiates with the design of a global sliding surface, which can eliminate the sliding mode approach process of the system and achieve global robustness in the system. Afterwards, a new switching function combined with the equivalent sliding mode control method is incorporated to effectively reduce the chattering generated when the system reaches the sliding mode manifold. Its asymptotic stability is proven without a priori knowledge on the bounds of unknown disturbances by using the Lyapunov stability theory. Lastly, the simulation conducted verifies the effectiveness and robustness of the GESMC proposed in this paper and meanwhile demonstrates a comparatively favorable performance for the GESMC in reducing chattering.

This paper presents WALLACE, a new framework of deep convolutional neural networks, which perform ConvNet's pyramidal feature hierarchy for weakly supervised learning. Most prior works rely on the image pyramid or network ensemble, which is both complicated and usually expensive. Instead, WALLACE is a more simple single-stage network that can predict objects present and location in an image without multiple rescale. Our model is trained efficiently using only global image-level labels, and it could generate meaningful multi-scale semantic feature maps by only one evaluation. Furthermore, a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps, which additional improve model performance. Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles.

Over the past decades, fossil fuel combustion has emitted large quantities of CO2 into the atmosphere, resulting in global climate change. Nowadays, it's considered a feasible strategy to capture CO2 from some significant point sources. Three main strategies have been developed, namely post-combustion, pre-combustion, and oxy-fuel combustion. Recent research indicates that the membrane technology for CO2 capture has become competitive compared with conventional technologies because of the improved separation performance in materials and process designs. This paper mainly reviews the progress and breakthroughs of membrane materials for the three gas separation systems corresponding to the CO2 capture strategies. Besides, the CO2 utilization by the membrane process has also been highlighted.

The reuse in high-value materials is one of the important resource utilization approaches of phosphorus tailings. At present, a mature technical system has been formed on the reuse of phosphorus slag in building materials, and silicon fertilizers in the extraction of yellow phosphorus. But there is a lack of research on the high-value reuse of phosphorus tailings. In order to make safe and effective utilization of phosphorus tailing resources, this research concentrated on how to solve easy agglomeration and difficult dispersion of phosphorus tailing micro-powder, when it was recycled in road asphalt. In the experimental procedure, phosphorus tailing micro-powder is treated in two methods. One method is to directly add it with different contents in asphalt to form a mortar. Dynamic shear tests were used to explore the effect of phosphorus tailing micro-powder on the high-temperature rheological properties of asphalt influence mechanism of material service behavior. The other method is to replace the mineral powder in asphalt mixture. The effect of phosphate tailing micro-powder on the water damage resistance in open-graded friction course (OGFC) asphalt mixtures was illustrated, based on the Marshall stability test and the freeze–thaw split test. The research results show that the performance indicators of the modified phosphorus tailing micro-powder meet the requirements for mineral powder in road engineering. Compared with standard OGFC asphalt mixtures, the residual stability of immersion and freeze–thaw splitting strength were improved when replace the mineral powder. The residual stability of immersion increased from 84.70% to 88.31%, and freeze–thaw splitting strength increased from 79.07% to 82.61%. The results indicate that phosphate tailing micro-powder has a certain positive effect on the water damage resistance. These performance improvements can be attributed to the larger specific surface area for phosphate tailing micro-powder than ordinary mineral powder, which can effectively adsorb asphalt and form structural asphalt. The research results are expected to support the large-scale reuse of phosphorus tailing powder in road engineering.

Fiber can absorb asphalt binder and therefore reinforce and stabilize the asphalt mixture structure and also prevent the asphalt from the leaking, which occurs in the process of mixing and transport. In this study, three kinds of fiber (polyester fiber, polypropylene fiber, and lignin fiber) are used to evaluate the relationship between the fiber types and mechanic performance of SMA-13 fiber asphalt mixture, which is specially designed for field tests of high-speed vehicles on pavements. The micro-surface characteristics of fiber and aggregates were studied by SEM and image analysis. Marshall stability and splitting strength were used to measure the properties of the asphalt mixture. In addition, a field test, including measures for curve-section edge, curve-section center, straight-section edge, and straight-section center, was conducted to evaluate the skid resistance of the high-speed vehicles that test field pavement. The results show that the Marshall stabilities of asphalt mixture with three kinds of fibers have been improved, whereas the stability of asphalt mixture prepared by polypropylene fiber and polyester fiber particularly increased before immersion. Among the three kinds of fiber asphalt mixtures, the polyester fiber asphalt mixture has enhanced water susceptibility. Skid resistance in the field test indicated that high skid resistance and good surface-texture depth were achieved.

The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores.

El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.

This work reviews the current progress of tight-binding methods and the recent edge-modified mean-field Hubbard model. Undercoordinated atoms (atoms not fully coordinated) exist at a high rate in nanomaterials with their impact overlooked. A quantum theory was proposed to calculate electronic structure of nanomaterials by incorporating bond order-length-strength (BOLS) correlation to mean-field Hubbard model, i.e., BOLS-HM. Consistency between the BOLS-HM calculation and density functional theory (DFT) calculation on 2D materials verified that (i) bond contractions and potential well depression occur at the edge of graphene, phosphorene, and antimonene nanoribbons; (ii) the physical origin of the band gap opening of graphene, phosphorene, and antimonene nanoribbons lays in the enhancement of edge potentials and hopping integrals due to the shorter and stronger bonds between undercoordinated atoms; (iii) the band gap of 2D material nanoribbons expand as the width decreases due to the increasing under-coordination effects of edges which modulates the conductive behaviors; and (iv) non-bond electrons at the edges and atomic vacancies of 2D material accompanied with the broken bond contribute to the Dirac-Fermi polaron (DFP) with a local magnetic moment.

In this work, we have successfully designed ordered luminescent multilayer films based on La-doped nonmagnetic or magnetic inorganic nanostructure with electronic microenvironment (EM). The inorganic nanosheets with opposite charge can assemble EM between the interlayers. At the same time, their elements on nanosheets of layer double hydroxides (LDHs) are facile to be replaced so that we can introduce transition metal or lanthanide elements. Besides, ferromagnetic effect (FE) can be formed in this microenvironment due to introducing transition metal on LDHs nanosheets. As a result, we confirm that EM, FE, and doping La element in the LDHs can affect the vibration of backbone of chromophores and then prolong the luminescent lifetime, which suggests a new pathway for developing the novel light-emitting thin films.

Supercapacitors have been one of the highest potential candidates for energy storage because of their significant advantages beyond rechargeable batteries in terms of large power density, short recharging time, and long cycle lifespan. In this work, Cu–Co sulfides with uniform flower-like structure have been successfully obtained via a traditional two-step hydrothermal method. The as-fabricated Cu–Co sulfide vulcanized from precursor (P–Cu–Co sulfide) is able to deliver superior specific capacitance of 592 F g−1 at 1 A g−1 and 518 F g−1 at 10 A g−1 which are surprisingly about 1.44 times and 2.39 times higher than those of Cu–Co oxide electrode, respectively. At the same time, excellent cycling stability of P–Cu–Co sulfide is indicated by 90.4% capacitance retention at high current density of 10 A g−1 after 3000 cycles. Because of the introduction of sulfur during the vulcanization process, these new developed sulfides can get more flexible structure and larger reaction surface area, and will own richer redox reaction sites between the interfaces of active material/electrolyte. The uniform flower-like P–Cu–Co sulfide electrode materials will have more potential alternatives for oxides electrode materials in the future.

Layered double hydroxides (LDHs), luminescent π-conjugated anionic polymer and montmorillonite (MMT) were orderly assembled into luminescent multilayer films via layer-by-layer self-assembly method. The electronic microenvironment (EME), the structure of which is like a traditional capacitor, can be constructed by exfoliated LDHs or MMT nanosheets. In addition, the rigid inorganic laminated configuration can offer stable surroundings between the interlayers. As a result, we conclude that EME can extend the luminescent lifespans of multilayer films substantially, due to affecting relaxation times of π-conjugated anionic polymer. Consequently, because of the remarkable impact on better photoemission behaviors of luminescent π-conjugated anionic polymer, EME assembled by LDHs or MMT nanosheets have had high hopes attached to them. They are expected to have the potential for designing, constructing, and investigating novel light-emitting thin films.