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Abstract To solve the problems of insufficient stiffness and poor integrity of traditional F-type socket joints, steel screw connections are set along the longitudinal direction between rectangular pipe jacking joints. However, the mechanical properties of F-type socket joints with steel screw connections have not been fully investigated, and the influence of the coefficient of subgrade reaction has not been considered. In this work, through model tests and numerical simulations of F-type socket joints with steel screws under different coefficients of subgrade reaction, the influence of steel screws on the deformation and damage characteristics of F-type socket joints is discussed, and the bending mechanical response of F-type socket joints under different coefficients of subgrade reaction is analyzed. Compared with traditional F-type socket joints, the use of steel screw connections can reduce the risk of steel ring warping and cross-sectional deformation of the joints and improve the stiffness and load-carrying capacity of the joints. However, the use of steel screw connections exacerbates damage to the chamfered parts of the jack joints. With an increase in the coefficient of subgrade reaction, the benefit of the steel screw on the joint stiffness gradually decreases. For every 0.5-fold increase in the coefficient of subgrade reaction, the effect of the steel screw on the joint bending load-carrying capacity of the joint is reduced by approximately 23%. The failure mode of joints containing steel screws is mainly the crushing of concrete in the compression zone, and the penetration cracks extend from the joint to the bottom of the pipe section. The higher the jacking force is, the higher the load-carrying capacity of the joint and the better the bending resistance.

Joints are the weakest part of rectangular pipe jacking tunnels, and the structural form of the joint is closely related to its bending resistance. In this work, the F-type socket joint of a rectangular pipe jacking tunnel is selected as the object of study. The bending mechanical properties of the joints connected by steel screws and those connected by bent bolts are compared via a three-point bending test. The results show that the two longitudinal connection joints have similar bending stiffnesses. Compared with the bent bolt connection joint, the steel screw connection joint has better toughness, and the load at which the joint enters the plastic stage and the bearing capacity are increased by 0.47 times and 1.02 times, respectively. The failure modes of the joints connected by steel screw connections and those connected by bent bolts are crushing of the concrete of the top plate and cracking of the concrete above the screw holes, respectively. When a bent bolt connection is used, the reinforcement at the screw hole should be locally strengthened, or ultrahigh-performance concrete (UHPC) should be used at the screw hole to improve the load-bearing capacity of the joint.

The efficient fusion of global and local multi-scale features is quite important for remote sensing scene classification (RSSC). The scenes in high-resolution remote sensing images (HRRSI) contain many complex backgrounds, intra-class diversity, and inter-class similarities. Many studies have shown that global features and local features are helpful for RSSC. The receptive field of a traditional convolution kernel is small and fixed, and it is difficult to capture global features in the scene. The self-attention mechanism proposed in transformer effectively alleviates the above shortcomings. However, such models lack local inductive bias, and the calculation is complicated due to the large number of parameters. To address these problems, in this study, we propose a classification model of global-local features and attention based on Lie Group space. The model is mainly composed of three independent branches, which can effectively extract multi-scale features of the scene and fuse the above features through a fusion module. Channel attention and spatial attention are designed in the fusion module, which can effectively enhance the crucial features in the crucial regions, to improve the accuracy of scene classification. The advantage of our model is that it extracts richer features, and the global-local features of the scene can be effectively extracted at different scales. Our proposed model has been verified on publicly available and challenging datasets, taking the AID as an example, the classification accuracy reached 97.31%, and the number of parameters is 12.216 M. Compared with other state-of-the-art models, it has certain advantages in terms of classification accuracy and number of parameters.

To address the problem of the expensive and time-consuming annotation of high-resolution remote sensing images (HRRSIs), scholars have proposed cross-domain scene classification models, which can utilize learned knowledge to classify unlabeled data samples. Due to the significant distribution difference between a source domain (training sample set) and a target domain (test sample set), scholars have proposed domain adaptation models based on deep learning to reduce the above differences. However, the existing models have the following shortcomings: (1) insufficient learning of feature information, resulting in feature loss and restricting the spatial extent of domain-invariant features; (2) models easily focus on background feature information, resulting in negative transfer; (3) the relationship between the marginal distribution and the conditional distribution is not fully considered, and the weight parameters between them are manually set, which is time-consuming and may fall into local optimum. To address the above problems, this study proposes a novel remote sensing cross-domain scene classification model based on Lie group spatial attention and adaptive multi-feature distribution. Concretely, the model first introduces Lie group feature learning and maps the samples to the Lie group manifold space. By learning features of different levels and different scales and feature fusion, richer features are obtained, and the spatial scope of domain-invariant features is expanded. In addition, we also design an attention mechanism based on dynamic feature fusion alignment, which effectively enhances the weight of key regions and dynamically balances the importance between marginal and conditional distributions. Extensive experiments are conducted on three publicly available and challenging datasets, and the experimental results show the advantages of our proposed method over other state-of-the-art deep domain adaptation methods.

Abstract Aqueous Zn-ion hybrid supercapacitors (ZHSs) are increasingly being studied as a novel electrochemical energy storage system with prominent electrochemical performance, high safety and low cost. Herein, high-energy and anti-self-discharge ZHSs are realized based on the fibrous carbon cathodes with hierarchically porous surface and O/N heteroatom functional groups. Hierarchically porous surface of the fabricated free-standing fibrous carbon cathodes not only provides abundant active sites for divalent ion storage, but also optimizes ion transport kinetics. Consequently, the cathodes show a high gravimetric capacity of 156 mAh g−1, superior rate capability (79 mAh g−1 with a very short charge/discharge time of 14 s) and exceptional cycling stability. Meanwhile, hierarchical pore structure and suitable surface functional groups of the cathodes endow ZHSs with a high energy density of 127 Wh kg−1, a high power density of 15.3 kW kg−1 and good anti-self-discharge performance. Mechanism investigation reveals that ZHS electrochemistry involves cation adsorption/desorption and Zn4SO4(OH)6·5H2O formation/dissolution at low voltage and anion adsorption/desorption at high voltage on carbon cathodes. The roles of these reactions in energy storage of ZHSs are elucidated. This work not only paves a way for high-performance cathode materials of ZHSs, but also provides a deeper understanding of ZHS electrochemistry.

BackgroundIncreased interferon (IFN)-gamma inducible protein-10 (IP-10) level has been shown to be associated with sustained virologic responses (SVRs) to pegylated interferon-alpha 2a/ribavirin-based therapy in patients with chronic hepatitis C (CHC). We investigated the relationship between IP-10 and treatment response in patients with CHC treated with direct-acting antiviral agents (DAAs) therapy.MethodsWe measured the dynamic changes of IP-10 in samples from 90 patients with CHC. The serum IP-10 levels, intrahepatic expressions of IP-10 mRNA, and protein were determined, respectively. For the in vitro experiments, the expression changes of IP-10 in hepatitis C virus (HCV)-replicating Huh-7 cells with or without non-structural protein 5A (NS5A) inhibitor were analyzed using real-time reverse transcription-polymerase chain reaction and Western blotting.ResultsPatients with chronic hepatitis C had increased baseline IP-10 levels, intrahepatic IP-10 mRNA, and protein expression. After initiating DAAs therapy, serum IP-10 levels decreased gradually in patients who achieved cure, whereas in patients who failed the therapy, IP-10 levels did not change significantly or recovered from the initial decline. Multivariate logistic regression analysis confirmed that baseline IP-10 level ≤ 450 pg/ml and decline >30% at 12 weeks independently predicted the SVR in patients with CHC who received DAAs. In vitro, the expression of IP-10 mRNA and protein in HCV-replicating Huh-7 cells increased significantly. However, such activities were downregulated by NS5A inhibitor, followed by the reduction of HCV RNA levels and a decline in IP-10 levels.ConclusionIP-10 interfered with HCV replication in hepatocytes and the dynamic decline in IP-10 levels during DAA treatment predicted the SVR in patients with CHC.

Understanding the effect of the space charge layer (SCL) in all-solid-state lithium-ion batteries is challenging due to lack of direct experimental observations. Here the authors visualize the SCL using an in-situ DPC-STEM imaging technique, based on which they further introduce a built-in electric field to suppress its formation.

Abstract Rechargeable aqueous zinc-ion batteries (ZIBs) have been gaining increasing interest for large-scale energy storage applications due to their high safety, good rate capability, and low cost. However, the further development of ZIBs is impeded by two main challenges: Currently reported cathode materials usually suffer from rapid capacity fading or high toxicity, and meanwhile, unstable zinc stripping/plating on Zn anode seriously shortens the cycling life of ZIBs. In this paper, metal–organic framework (MOF) materials are proposed to simultaneously address these issues and realize high-performance ZIBs with Mn(BTC) MOF cathodes and ZIF-8-coated Zn (ZIF-8@Zn) anodes. Various MOF materials were synthesized, and Mn(BTC) MOF was found to exhibit the best Zn2+-storage ability with a capacity of 112 mAh g−1. Zn2+ storage mechanism of the Mn(BTC) was carefully studied. Besides, ZIF-8@Zn anodes were prepared by coating ZIF-8 MOF material on Zn foils. Unique porous structure of the ZIF-8 coating guided uniform Zn stripping/plating on the surface of Zn anodes. As a result, the ZIF-8@Zn anodes exhibited stable Zn stripping/plating behaviors, with 8 times longer cycle life than bare Zn foils. Based on the above, high-performance aqueous ZIBs were constructed using the Mn(BTC) cathodes and the ZIF-8@Zn anodes, which displayed an excellent long-cycling stability without obvious capacity fading after 900 charge/discharge cycles. This work provides a new opportunity for high-performance energy storage system.

Aqueous zinc-ion batteries (ZIBs) are currently receiving widespread attention due to their merits of environmental-friendly properties, high safety, and low cost. However, the absence of stable zinc metal anodes severely restricts their potential applications. In this work, we demonstrate a simple oxygen plasma treatment method to modify the surface state of carbon cloth to construct an ideal substrate for zinc deposition to solve the dendrite growth problem of zinc anodes. The plasma treated carbon cloth (PTCC) electrode has lower nucleation overpotential and uniformly distributed C=O zincophilic nucleation sites, facilitating the uniform nucleation and subsequent homogeneous deposition of zinc. Benefiting from the superior properties of PTCC substrate, the enhanced zinc anodes demonstrate low voltage hysteresis (about 25 mV) and stable zinc plating/stripping behaviors (over 530 h lifespan) at 0.5 mA cm−2 with 15% depth of discharge (DOD). Besides, an extended cycling lifespan of 480 h can also be achieved at very high DOD of 60%. The potential application of the enhanced zinc anode is also confirmed in Zn|V10O24·12H2O full cell. The cells with Zn@PTCC electrode demonstrate remarkable rate capability and excellent cycling stability (95.0% capacity retention after 500 cycles).

Convolutional Neural Networks have been widely used in remote sensing scene classification. Since this kind of model needs a large number of training samples containing data category information, a Generative Adversarial Network (GAN) is usually used to address the problem of lack of samples. However, GAN mainly generates scene data samples that do not contain category information. To address this problem, a novel supervised adversarial Lie Group feature learning network is proposed. In the case of limited data samples, the model can effectively generate data samples with category information. There are two main differences between our method and the traditional GAN. First, our model takes category information and data samples as the input of the model and optimizes the constraint of category information in the loss function, so that data samples containing category information can be generated. Secondly, the object scale sample generation strategy is introduced, which can generate data samples of different scales and ensure that the generated data samples contain richer feature information. After large-scale experiments on two publicly available and challenging datasets, it is found that our method can achieve better scene classification accuracy even with limited data samples.

Remote sensing scene classification is quite important in earth observation and other fields. Previous research has found that most of the existing models are based on deep learning models. However, the classification accuracy of the deep learning model is difficult to break through due to the challenges of difficulty distinguishing the socio-economic attributes of scenes, high interclass similarity, and large intraclass differences. To tackle the challenges, we propose a novel scene classification model that integrates heterogeneous features of multi-source data. Firstly, a multi-granularity feature learning module is designed, which can conduct uniform grid sampling of images to learn multi-granularity features. In this module, in addition to the features of our previous research, we also supplemented the socio-economic semantic features of the scene, and attention-based pooling is introduced to achieve different levels of representation of images. Then, to reduce the dimension of the feature, we adopt the feature-level fusion method. Next, the maxout-based module is designed to fuse the features of different granularity and extract the most distinguishing second-order latent ontology essence features. The weighted adaptive fusion method is used to fuse all the features. Finally, the Lie Group Fisher algorithm is used for scene classification. Extensive experimentation and evaluations show that our proposed model can find better solutions to the above challenges.

Abstract Rechargeable aqueous zinc-ion hybrid capacitors and zinc-ion batteries are promising safe energy storage systems. In this study, amorphous RuO2·H2O for the first time was employed to achieve fast and ultralong-life Zn2+ storage based on a pseudocapacitive storage mechanism. In the RuO2·H2O||Zn zinc-ion hybrid capacitors with Zn(CF3SO3)2 aqueous electrolyte, the RuO2·H2O cathode can reversibly store Zn2+ in a voltage window of 0.4–1.6 V (vs. Zn/Zn2+), delivering a high discharge capacity of 122 mAh g−1. In particular, the zinc-ion hybrid capacitors can be rapidly charged/discharged within 36 s with a very high power density of 16.74 kW kg−1 and a high energy density of 82 Wh kg−1. Besides, the zinc-ion hybrid capacitors demonstrate an ultralong cycle life (over 10,000 charge/discharge cycles). The kinetic analysis elucidates that the ultrafast Zn2+ storage in the RuO2·H2O cathode originates from redox pseudocapacitive reactions. This work could greatly facilitate the development of high-power and safe electrochemical energy storage.

Abstract Aqueous rechargeable Zn/MnO2 zinc-ion batteries (ZIBs) are reviving recently due to their low cost, non-toxicity, and natural abundance. However, their energy storage mechanism remains controversial due to their complicated electrochemical reactions. Meanwhile, to achieve satisfactory cyclic stability and rate performance of the Zn/MnO2 ZIBs, Mn2+ is introduced in the electrolyte (e.g., ZnSO4 solution), which leads to more complicated reactions inside the ZIBs systems. Herein, based on comprehensive analysis methods including electrochemical analysis and Pourbaix diagram, we provide novel insights into the energy storage mechanism of Zn/MnO2 batteries in the presence of Mn2+. A complex series of electrochemical reactions with the co-participation of Zn2+, H+, Mn2+, SO4 2−, and OH− were revealed. During the first discharge process, co-insertion of Zn2+ and H+ promotes the transformation of MnO2 into Zn x MnO4, MnOOH, and Mn2O3, accompanying with increased electrolyte pH and the formation of ZnSO4·3Zn(OH)2·5H2O. During the subsequent charge process, Zn x MnO4, MnOOH, and Mn2O3 revert to α-MnO2 with the extraction of Zn2+ and H+, while ZnSO4·3Zn(OH)2·5H2O reacts with Mn2+ to form ZnMn3O7·3H2O. In the following charge/discharge processes, besides aforementioned electrochemical reactions, Zn2+ reversibly insert into/extract from α-MnO2, Zn x MnO4, and ZnMn3O7·3H2O hosts; ZnSO4·3Zn(OH)2·5H2O, Zn2Mn3O8, and ZnMn2O4 convert mutually with the participation of Mn2+. This work is believed to provide theoretical guidance for further research on high-performance ZIBs.

Discriminative feature learning is the key to remote sensing scene classification. Previous research has found that most of the existing convolutional neural networks (CNN) focus on the global semantic features and ignore shallower features (low-level and middle-level features). This study proposes a novel Lie Group deep learning model for remote sensing scene classification to solve the above-mentioned challenges. Firstly, we extract shallower and higher-level features from images based on Lie Group machine learning (LGML) and deep learning to improve the feature representation ability of the model. In addition, a parallel dilated convolution, a kernel decomposition, and a Lie Group kernel function are adopted to reduce the model’s parameters to prevent model degradation and over-fitting caused by the deepening of the model. Then, the spatial attention mechanism can enhance local semantic features and suppress irrelevant feature information. Finally, feature-level fusion is adopted to reduce redundant features and improve computational performance, and cross-entropy loss function based on label smoothing is used to improve the classification accuracy of the model. Comparative experiments on three public and challenging large-scale remote-sensing datasets show that our model improves the discriminative ability of features and achieves competitive accuracy against other state-of-the-art methods.

Heat pipe heat exchangers (HPHEXs) are widely used in various industries. In this paper, a novel model of a liquid–liquid heat pipe heat exchanger in a countercurrent manner is established by considering the evaporation and condensation thermal resistances inside the heat pipes (HPs). The discrete method is added to the HPHEX model to determine the thermal resistances of the HPs and the temperature change trend of the heat transfer fluid in the HPHEX. The established model is verified by the HPHEX structure and experimental data in the existing literature and demonstrates numerical results that agree with the experimental data to within a 5% error. With the current model, the investigation compares the effectiveness and minimum vapor temperature of the HPHEX with three types of HP diameters, different mass flow rates, and different H* values. For HPs with a diameter of 36 mm, the effectiveness of each is improved by about 0.018 to 0.029 compared to HPs with a diameter of 28 mm. The results show that the current model can predict the temperature change trend of the HPHEX well; in addition, the effects of different structures on the effectiveness and minimum vapor temperature are obtained, which improve the performance of the HPHEX.

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms.

Recent years, the research on object classification based on three-dimensional (3D) point cloud pays more attention to extract the features from point sets directly. PointNet++ is the latest network structure for 3D classification which has achieved acceptable results. Although it has achieved acceptable results, there are still two problems: (i) The farthest point sampling (FPS) algorithm in PointNet++ ignores the fact that the feature of each point contributes differently to the classification and segmentation tasks. Therefore, FPS cannot guarantee that the selected point sets can correctly represent the main features of the object. (ii) The multi-scale grouping and multi-resolution grouping in PointNet++ do not consider the features between different levels of the same point. In order to solve these problems, the authors propose the point-selection structure which can calculate the importance of each point's feature. In addition, multi-level-point-feature fusion module is proposed to extract the features of the point at all levels and fuse them as a new feature of that point. In this Letter, they make some experiments on ModelNet40 and ScanNet datasets, which achieves better results compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Poly(ethylene oxide) (PEO) nanofibers were prepared by electrospinning PEO solution with a mixed solvent of ethanol and deionized water. The results show that the mixed solvent system has noteworthy influences on structures and properties of electrospun PEO nanofibers, including molecular chain orientation, crystallinity degree, surface morphology, fiber diameter, diameter distribution, spinnability, and productivity. With increasing ethanol content in the mixed solvent, wrinkly morphologies appear on the surface of PEO nanofibers due to a high evaporation rate of ethanol during electrospinning process. The dielectric constant, dipole moment, conductivity, density, boiling point, and solubility parameter of the mixed solvent become lower with the ethanol content increasing. Besides, the hydrogen-bonding interactions between PEO and solvents become weaker. As a result, PEO nanofibers with larger diameters, lower molecular chain orientation, and crystallinity degree are obtained. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 45787. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the applicability of adenosine deaminase (ADA), α-l-fucosidase (AFU), lactic acid (LAC), and their combined detection in the early diagnosis of chronic hepatitis B (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC).Methods: A retrospective analysis of hepatitis B-positive liver disease patients admitted between 2015 and 2020 was conducted. The receiver operating characteristic (ROC) curve was used to determine the diagnostic value of each indicator in LC and HCC, and binary logistic regression analysis was performed to determine the factors and risks related to the occurrence of the two conditions.Results: The levels of ADA, AFU, and LAC were significantly increased in patients with CHB, LC, and HCC (p < 0.05). The ROC curve showed that the sensitivity and specificity of ADA, AFU, LAC, and their combined detection in the CHB and LC groups as well as in the LC and HCC groups reflected different degrees of clinical value. In the CHB and LC groups, the adjusted odds ratio (OR) values of ADA, AFU, and LAC among patients in the high-level group were 3.218, 1.859, and 11.474, respectively, when the median was considered the cutoff point. When quartiles were considered the cutoff point, the OR risk values of the adjusted levels of ADA, AFU, and LAC were higher than those of the lowest-level group (Q1) (p < 0.05). In the LC and HCC groups, the adjusted OR values of ADA, AFU, and LAC among patients in the high-level group were 0.967, 2.365, and 38.368, respectively. When quartiles were considered the cutoff point, the OR risk values of AFU and LAC levels were higher than those of the lowest-level group (Q1) (p < 0.05).Conclusion: ADA, AFU, and LAC demonstrated good value in the early diagnosis of LC and HCC. The combined detection of ADA+AFU+LAC is more effective than single detection for the early diagnosis of the two conditions. ADA, AFU, and LAC can serve as risk predictors of LC, while AFU and LAC can be considered early risk predictors of HCC.

A novel composite anode is prepared by mixing zinc particles with activated carbon (AC) to improve the cycle performance of the neutral rechargeable zinc ion batteries. Galvanostatic charge/discharge cycling tests indicate that the capacity retention of the cell with adding 12 wt% activated carbon in Zn anode is 85.6% after 80 cycles, which is much higher than that of 56.7% for the cell using unmodified Zn anode. X-ray diffraction analysis indicates that the addition of activated carbon can suppress the formation of inactive basic zinc sulfates (Zn4SO4(OH)6 ⋅ nH20). Morphology, elemental mapping and N2 adsorption and desorption measurements indicate that the pores of activated carbon can accommodate the deposition of Zn dendrites and insoluble anodic products. As a result, the cycle stability of the Zn anode has been greatly enhanced by activated carbon modification. [ABSTRACT FROM AUTHOR]

The storage process of Zn2+ ion into α-MnO2 has been investigated by electrochemical impedance spectrum. A new phase formation process has been recognized. The equivalent circuit model and the impedance expression have been built up to characterize the storage of Zn2+ ion into α-MnO2. It is found that the storage process of Zn2+ ions is mainly determined by the phase change. With the decrease of potential, Zn2+ ions begin to insert into MnO2 and phase change occurs, which leads to a significant increase on phase change resistance (Rτ). We also investigated the relationship between the phase change and the phase angle. It is mathematically calculated that maximum phase angle (|Φ|0) at low frequency domain shows a logarithmic dependence on Rτ, which is confirmed by the experimental results. By using |Φ|0 directly from Bode plot, we can easily monitor the phase change and the storage process of zinc ion into MnO2. [ABSTRACT FROM AUTHOR]

The phase compositions of the LiFePO4-based cathode materials upon varying the adding amount of vanadium x from 0 to 0. 13 are systemically investigated. A corresponding concentration-composition phase diagram is presented, and the x-dependent variations in the physiochemical and electrochemical performances are systemically analyzed. In the whole concentration range, the chemical valence of Fe2+ remains invariant whereas the valence of vanadium evolves from +4 to +3. With the increase of adding amount of vanadium, the electrical conductivity of samples varies nonlinearly as an "N" shape while the lithium ion diffusion coefficient first increases and then decreases. The doping of vanadium (x ⩽0.07) is beneficial to refine the particle size and increase the electrical conductivity. Upon doping, the reversibility of the electrochemical reaction and the rate capability are monotonically enhanced. The conductive VO2(B) modifies the interface property of nanoparticles, and the sample achieves the high power density without sacrificing the energy density due to pesudocapacitive behavior. The formation of Li3V2(PO4)3 (x⩾ 0.11) is more beneficial to improve the rate capacity of the LiFePO4-based cathode, although it undermines the lithium-ion transport of the main phase. By optimizing the content of Li3V2(PO4)3, excellent rate capacity and high specific capacity can be achieved. [ABSTRACT FROM AUTHOR]

Manganese dioxide shows an ideal capacitive behavior in the aqueous solution containing bivalent cations (Ca2+). A specific capacitance value as high as 310 F g-1 in 0.1 mol L-1 Ca(NO3)2 electrolyte was measured at a sweep rate of 2 mV s-1. A cycle life test was also performed and no capacity loss of MnO2 electrode was found after 600 cycles, which indicates the MnO2 electrode possesses a good cycling property in the aqueous electrolyte containing bivalent cations (Ca2+). X-ray photoelectron spectroscopy data reveal that the Ca2+ ions in the electrolyte are involved in the charge storage process of manganese dioxide in an aqueous system and correspondingly a part of Mn4+ ions will become to Mn3+ ion to balance the charge. [ABSTRACT FROM AUTHOR]

The increasing worldwide interest in MnO2 for supercapacitor applications is based on anticipation that MnO2-based high-voltage aqueous supercapacitors will ultimately serve as a safe and low-cost alternative to state-of-the-art commercial organic-based electrochemical double-layer capacitors or RuO2-based acid systems. In this paper, the physicochemical features, synthesis methods, and charge storage mechanism of MnO2 as well as the current status of MnO2-based supercapacitors are summarized and discussed in detail. The future opportunities and challenges related to MnO2-based supercapacitors have also been proposed. [ABSTRACT FROM AUTHOR]