Integration: An Effective Strategy to Develop Multifunctional Energy Storage Devices

Energy storage devices are arousing increasing interest due to their key role in next‐generation electronics. Integration is widely explored as a general and effective strategy aiming at high performances. Recent progress in integrating a variety of functions into electrochemical energy storage devices is carefully described. Through integration at the level of materials: flexible, stretchable, responsive, and self‐healing devices are discussed to highlight the state‐of‐the‐art multi‐functional electronics. Through the integration at the level of devices, the incorporation of photovoltaic and piezoelectric devices is detailed to reflect the advances in self‐powering electronics. Integrated energy storage devices are presented for wearable applications to indicate a new growth direction. The main challenges and important directions are summarized to offer some useful clues for future development.     

A Fully Verified Theoretical Analysis of Contact‐Mode Triboelectric Nanogenerators as a Wearable Power Source

Harvesting mechanical energy from human activities by triboelectric nanogenerators (TENGs) is an effective approach for sustainable, maintenance‐free, and green power source for wireless, portable, and wearable electronics. A theoretical model for contact‐mode triboelectric nanogenerators based on the principles of charge conservation and zero loop‐voltage is illustrated. Explicit expressions for the output current, voltage, and power are presented for the TENGs with an external load of resistance. Experimental verification is conducted by using a laboratory‐fabricated contact‐mode TENG made from conducting fabric electrodes and polydimethylsiloxane/graphene oxide composite as the dielectric layer. Excellent agreements of the output voltage, current, and power are demonstrated between the theoretical and experimental results, without any adjustable parameters. The effects of the moving speed on output voltage, current, and power are illustrated in three cases, that is, the motion with constant speed, the sinusoidal motion cycles, and the real walking cycles by human subject. The fully verified theoretical model is a very powerful tool to guide the design of the device structure and selection of materials, and optimization of performance with respect to the application conditions of TENGs.     

Two‐Dimensional Materials for Beyond‐Lithium‐Ion Batteries

Lithium‐ion batteries (LIBs) have dominated the portable electronics industry and solid‐state electrochemical research and development for the past two decades. In light of possible concerns over the cost and future availability of lithium, sodium‐ion batteries (SIBs) and other new technologies have emerged as candidates for large‐scale stationary energy storage. Research in these technologies has increased dramatically with a focus on the development of new materials for both the positive and negative electrodes that can enhance the cycling stability, rate capability, and energy density. Two‐dimensional (2D) materials are showing promise for many energy‐related applications and particularly for energy storage, because of the efficient ion transport between the layers and the large surface areas available for improved ion adsorption and faster surface redox reactions. Recent research highlights on the use of 2D materials in these future ‘beyond‐lithium‐ion’ battery systems are reviewed, and strategies to address challenges are discussed as well as their prospects.     

Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Multivariate synchronization index (MSI) has been proved to be an efficient method for frequency recognition in SSVEP-BCI systems. It measures the correlation according to the entropy of the normalized eigenvalues of the covariance matrix of multichannel signals. In the MSI method, the estimation of covariance matrix omits the temporally local structure of samples. In this study, a new spatio-temporal method, termed temporally local MSI (TMSI), was presented. This new method explicitly exploits temporally local information in modelling the covariance matrix. In order to evaluate the performance of the TMSI, we performs a comparison between the two methods on the real SSVEP datasets from eleven subjects. The results show that the TMSI outperforms the standard MSI. TMSI benefits from exploiting the temporally local structure of EEG signals, and could be a potential method for robust performance of SSVEP-based BCI.