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.     

Comparative proteomics analysis of silkworm hemolymph during the stages of metamorphosis via liquid chromatography and mass spectrometry

The silkworm is a lepidopteran insect that has an open circulatory system with hemolymph consisting of blood and lymph fluid. Hemolymph is not only considered as a depository of nutrients and energy, but it also plays a key role in substance transportation, immunity response, and proteolysis. In this study, we used LC‐MS/MS to analyze the hemolymph proteins of four developmental stages during metamorphosis. A total of 728 proteins were identified from the hemolymph of the second day of wandering stage, first day of pupation, ninth day of pupation, and first day as an adult moth. GO annotations and categories showed that silkworm hemolymph proteins were enriched in carbohydrate metabolism, proteolysis, protein binding, and antibacterial humoral response. The levels of nutrient, immunity‐related, and structural proteins changed significantly during development and metamorphosis. Some, such as cuticle, odorant‐binding, and chemosensory proteins, showed stage‐specific expression in the hemolymph. In addition, the expression of several antimicrobial peptides exhibited their highest level of abundance in the hemolymph of the early pupal stage. These findings provide a comprehensive proteomic insight of the silkworm hemolymph and suggest additional molecular targets for studying insect metamorphosis.     

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.