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Flexible solid-state battery has several unique characteristics including high flexibility, easy portability, and high safety, which may have broad application prospects in new technology products such as rollup displays, power implantable medical devices, and wearable equipments. The interfacial mechanical and electrochemical problems caused by bending deformation, resulting in the battery damage and failure, are particularly interesting. Herein, a fully coupled electro-chemo-mechanical model is developed based on the actual solid-state battery structure. Concentration-dependent material parameters, stress-dependent diffusion, and potential shift are considered. According to four bending forms (k = 8/mm, 0/mm, −8/mm, and free), the results show that the negative curvature bending is beneficial to reducing the plastic strain during charging/discharging, while the positive curvature is detrimental. However, with respect to the electrochemical performance, the negative curvature bending creates a negative potential shift, which causes the battery to reach the cut-off voltage earlier and results in capacity loss. These results enlighten us that suitable electrode materials and charging strategy can be tailored to reduce plastic deformation and improve battery capacity for different forms of battery bending. [ABSTRACT FROM AUTHOR]

In order to help the operator perform the human-robot collaboration task and optimize the task performance, an adaptive control method based on optimal admittance parameters is proposed. The overall control structure with the inner loop and outer loop is first established. The tasks of the inner loop and outer loop are robot control and task optimization, respectively. An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed, which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator. Subsequently, the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force. The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model. The model includes the unknown dynamics of the operator and the task performance details. To relax the requirement of the system model, the integral reinforcement learning is employed to solve the linear quadratic regulator problem. Besides, an auxiliary force is designed to help the operator complete the specific task better. Compared with the traditional control scheme, the security performance and interaction performance of the human-robot collaboration system are improved. The effectiveness of the proposed method is verified through two numerical simulations. In addition, a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of educational data mining and learning analytics, this study tries to make sense of education data and improve teachers' competence and teaching experience. In recent years, massive open online courses (MOOCs) have become the first choice of online learning for tens of millions of people around the world. However, the dropout rates for MOOCs are high. The goal of dropout prediction is to predict whether learners will exhibit learning behavior in several consecutive days in the future. Therefore, in this study, we consider the correlation information of learners' learning behaviors for several consecutive days. Through the in-depth statistical analysis of learners' learning behavior, it is found that learners' learning behavior on the next day is similar to that of the previous day. Based on this characteristic, we propose a Lie group region covariance matrix to represent the local correlation information of learning behavior and construct a convolutional neural network model with a multidilation pooling module to extract the local correlation high-level features of learning behavior for dropout prediction. In addition, extensive experiments show that the local correlation of learners' learning behavior cannot be ignored, which is fully considered in our model. Compared with the existing methods, our method achieves the best experimental results in accuracy, F-measure, precision, and recall, which is better than the current methods. [ABSTRACT FROM AUTHOR]

Due to the improvement of network infrastructure and the application of Internet of Things equipment, a large number of sensors are deployed in the industrial pipeline production, and the large size of data is generated. The most typical case in the production line is product inspection, that is, defect inspection. To implement an efficient and robust detection system, in this study, we propose a classification computing model based on Lie Group Machine Learning, which can find the possible defective products in production. Usually, a workshop has a lot of assembly lines. How to process large data on so many production lines in real-time and accurately is a difficult problem. To solve this problem, we use the concept of fog computing to design the system. By offloading the computation burden from the cloud server center to the fog nodes, the system obtains the ability to deal with extremely data. Our system has two obvious advantages. The first one is to apply Lie Group Machine Learning to fog computing environment to improve the computational efficiency and robustness of the system. The other is that without increasing any production costs, it can quickly detect products, reduce network latency, and reduce the load on bandwidth. The simulations prove that, compared with the existing methods, the proposed method has an average running efficiency increase of 52.57%, an average delay reduction of 42.13%, and an average accuracy increase of 27.86%. [ABSTRACT FROM AUTHOR]

Highlights: A surface engineering strategy was proposed to design hierarchically porous structure on fibrous carbon cathodes with O/N heteroatom functional groups. High-energy and anti-self-discharge Zn-ion hybrid supercapacitors (ZHSs) were realized. ZHS electrochemistry was investigated and new insights were provided.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. [ABSTRACT FROM AUTHOR]

In practical application, the data are imbalanced, it is difficult to find the balanced, rather skewed data is the common occurrence. This poses a severe challenge to the classification algorithm. At present, imbalanced data classification methods are mainly for binary classes designed, and it is difficult to extend them to multiple classes. In this study, we introduced Lie group machine learning and proposed a semi-supervised learning algorithm based on the linear Lie group. First, the sample set is represented by a matrix, the isomorphism(or homomorphism)-GL(n) linear Lie group of the corresponding learning system is found, and the labeled data are used to represent the object to be learned by linear Lie group. Then, according to the algebraic structure of the linear Lie group, it is marked by the group method. We performed experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed method and measured the performance of multi-class imbalanced data using four state-of-the-art learning algorithms (mean of accuracy, mean of f-measure, and mean of area under the curve). The experimental results demonstrate that the proposed method is effective and improves the performance. [ABSTRACT FROM AUTHOR]

Diffusion-induced stress caused by the insertion and extraction of lithium ions can result in the swelling, fracture, and even pulverization of the battery electrodes. However, only a few previous studies consider the phenomenon of phase transformation in an electrode and rarely take the impacts of cylindrical shape with transversely isotropic properties into account. In this paper, by researching the electronic reaction and diffusion process, a new theoretical model is established to study the stress level and mechanical behavior in cylindrical electrodes with phase transformation under galvanostatic operation. From the model, the brittle center and edge cracks are analyzed to investigate the influence of the initiation position on crack propagation. The tangential stress plays an important role in cracking on the electrodes. Furthermore, it is found for the center crack that it tends to grow more easily in the first insertion when the crack locates at the phase interface position, while for the edge crack, it tends to grow more easily in the early stage of lithium ion extraction. Moreover, manufacturing the electrodes with the appropriate property ratios, the diffusion-induced stress level and brittle crack-induced stress intensity factor value may decrease, and the electrode fracture phenomenon could be alleviated to some degree. Overall, our work provides a theoretical basis for the electrode phase transformation and cracking when the battery is working, and it may help us understand more about the internal mechanical behavior of the battery electrodes. [ABSTRACT FROM AUTHOR]

Highlights: Various MOF materials were synthesized and investigated as ZIB cathodes. A long-term stable ZIF-8@Zn anode was proposed by coating ZIF-8 material on the surface of zinc foils. High-performance aqueous ZIBs were constructed using the Mn(BTC) cathode and the ZIF-8@Zn anode.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. [ABSTRACT FROM AUTHOR]

Developing alternative oxygen reduction reaction (ORR) catalysts to replace precious Pt-based metals with abundant materials is the key challenge of commercial application of fuel cells. Owing to their various compositions and tunable electronic properties, transition metal dichalcogenides (TMDs) have the great potential to realize high-efficiency catalysts for ORR. Here, various 3R-phase dichalcogenides of group VB and VIB transition metals (MX2, M = Nb, Ta, Mo, W; X = S, Se, Te) are investigated for ORR catalysts by using density functional theory calculations. The computed over-potentials of group VB TMDs are much less than those of group VIB TMDs. For group VB TMDs, a volcano-type plot of ORR catalytic activity is established on the adsorption energies of *OH, and NbS2 and TaTe2 exhibit best ORR activity with an over-potential of 0.54 V. To achieve even better activity, strain engineering is performed to tune ORR catalytic activity, and the minimum over-potential of 0.43 V can be realized. We further demonstrate that the shift of p orbital center of surface chalcogen elements under strain is responsible for tuning the catalytic activity of TMDs. [ABSTRACT FROM AUTHOR]

Hybrid metal-ion capacitors are designed to promote the energy density of supercapacitors with less sacrifice of power density. Zinc-ion hybrid supercapacitor, based on the multivalent ion storage principle, is a kind of energy storage device in which both the high energy density and power density can be achieved. Here, we propose a new configuration of zinc-ion hybrid supercapacitors composed of mild aqueous ZnSO4 electrolyte, activated carbon (AC) anode and V2O5 cathode. The operating voltage of the hybrid supercapacitor can reach to 2 V in the aqueous electrolyte when the mass ratio of AC to V2O5 is 1:1. The maximum energy density of zinc-ion hybrid capacitor is about 3.9 times higher than that of AC symmetric supercapacitor, while its maximum power density is 1.7 times higher than that of zinc-ion battery. The capacity retention of the hybrid supercapacitors is 97.3% over 6000 charge-discharge cycles at 0.5 A g−1. Compared with MnO2 zinc-ion hybrid supercapacitors system, the stable nature of V2O5 allows new zinc-ion hybrid supercapacitors system to achieve a better cycling performance. The unique electrochemical performance, low cost and high safety of the new zinc-ion hybrid supercapacitor endow it with a very wide range of applications in consumer electronics and stationary energy storage. [ABSTRACT FROM AUTHOR]

With the fast development of portable and wearable devices, flexible supercapacitor electrodes are widely researched. Here, comprehensive approaches were designed to introduce carbon nanotube (CNT) and/or MnO into activated carbon fiber felt (ACFF) using 'dipping and drying' method. Differences on micro-morphologies and electrochemical characteristics for prepared textiles were compared. High-performance flexible MnO/CNT/ACFF composite electrodes were synthesized by introducing CNT and MnO/CNT fillers successively. Compared with original ACFF textiles, significant improvements in electrochemical performance were achieved. Areal capacitance, energy density and power density of the composite textiles reached as high as 4148 mF cm, 141 μWh cm and 4466 μW cm, respectively. Furthermore, flexible supercapacitors were fabricated based on the composite textile electrodes and gel electrolytes. When being bent at different angles or suffering deformations such as bending for 100 cycles, the flexible supercapacitors preserve almost all the capacitance, which indicates the excellent flexibility of the composite textile electrode. This work provides various approaches to design composite textiles, and the prepared MnO/CNT/ACFF composite textile may be a promising electrode material for high-performance flexible supercapacitors. [ABSTRACT FROM AUTHOR]

An inorganic and non-toxic compounds combination of FeCl·4HO, LiCO and HPO was chosen to synthesize homogeneous nano-structured LiFePO/C composite material via a simplified sol-gel route. The dependency of the physicochemical properties and the corresponding electrochemical responses on the residual carbon content were investigated in details. Rietveld refinement of X-ray diffraction measurement and X-ray photoelectron spectroscopy analysis confirmed the feasibility of preparing pure LiFePO phase via this approach. With increasing amount of residual carbon, the particles size gradually decreased and the bulk electrical conductivity monotonically increased. However, the higher level of residual carbon would bring disadvantageous impact on the lithium ion diffusion. Due to high electrical conductivity, controlled particle size and suitable microstructure, the sample with 4.5 wt.% residual carbon exhibited stable cycling performance and delivered high discharge capacity of 163, 119 and 108 mA h g at 0.1 C, 5 C and 10 C, respectively. [ABSTRACT FROM AUTHOR]

Nanocrystalline LiFePO and LiFeSnPO cathode materials were synthesized by an inorganic-based sol-gel route. The physicochemical properties of samples were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, and elemental mapping. The doping effect of Sn on the electrochemical performance of LiFePO cathode material was extensively investigated. The results showed that the doping of tin was beneficial to refine the particle size, increase the electrical conductivity, and facilitate the lithium-ion diffusion, which contributed to the improvement of the electrochemical properties of LiFePO, especially the high-rate charge/discharge performance. At the low discharge rate of 0.5 C, the LiFeSnPO sample delivered a specific capacity of 158 mAh g, as compared with 147 mAh g of the pristine LiFePO. At higher C-rate, the doping sample exhibited more excellent discharge performance. LiFeSnPO delivered specific capacity of 146 and 128 mAh g at 5 C and 10 C, respectively, in comparison with 119 and 107 mAh g for LiFePO. Moreover, the doping of Sn did not influence the cycle capability, even at 10 C. [ABSTRACT FROM AUTHOR]

The space charge layer (SCL) is generally considered one of the origins of the sluggish interfacial lithium-ion transport in all-solid-state lithium-ion batteries (ASSLIBs). However, in-situ visualization of the SCL effect on the interfacial lithium-ion transport in sulfide-based ASSLIBs is still a great challenge. Here, we directly observe the electrode/electrolyte interface lithium-ion accumulation resulting from the SCL by investigating the net-charge-density distribution across the high-voltage LiCoO2/argyrodite Li6PS5Cl interface using the in-situ differential phase contrast scanning transmission electron microscopy (DPC-STEM) technique. Moreover, we further demonstrate a built-in electric field and chemical potential coupling strategy to reduce the SCL formation and boost lithium-ion transport across the electrode/electrolyte interface by the in-situ DPC-STEM technique and finite element method simulations. Our findings will strikingly advance the fundamental scientific understanding of the SCL mechanism in ASSLIBs and shed light on rational electrode/electrolyte interface design for high-rate performance ASSLIBs. 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 FROM AUTHOR]

The use of electricity generated from clean and renewable sources, such as water, wind, or sunlight, requires efficiently distributed electrical energy storage by high-power and high-energy secondary batteries using abundant, low-cost materials in sustainable processes. American Science Policy Reports state that the next-generation 'beyond-lithium' battery chemistry is one feasible solution for such goals. Here we discover new 'multivalent ion' battery chemistry beyond lithium battery chemistry. Through theoretic calculation and experiment confirmation, stable thermodynamics and fast kinetics are presented during the storage of multivalent ions (Ni2+, Zn2+, Mg2+, Ca2+, Ba2+, or La3+ ions) in alpha type manganese dioxide. Apart from zinc ion battery, we further use multivalent Ni2+ ion to invent another rechargeable battery, named as nickel ion battery for the first time. The nickel ion battery generally uses an alpha type manganese dioxide cathode, an electrolyte containing Ni2+ ions, and Ni anode. The nickel ion battery delivers a high energy density (340 Wh kg−1, close to lithium ion batteries), fast charge ability (1 minute), and long cycle life (over 2200 times). [ABSTRACT FROM AUTHOR]