Highly conductive dodecaborate/MXene composites for high performance supercapacitors

With the increasingly prominent energy and environmental issues,the supercapacitors,as a highly efficient and clean energy conversion and storage devices,meet the requirements well.However,it is still a challenge to enhance the capacitance and energy density of supercapacitors.A novel and highly conductive dodecaborate/MXene composites have been designed for high performance supercapacitors.The surface charge property of MXene was modified by a simple ultrasonic treatment with ammonium ion,and the dodecaborate ion can be inserted into the inner surface of MXene by electrostatic adsorption.Due to the unique icosahedral cage conjugate structure formed by the B-B bond and the highly delocalized three-dimensionalπbond structure of the electrons,the negative charge is delocalied on the whole dodecaborate ion,which reduces the ability to bind to cations.Therefore,the cations can move easily,and the dodecaborate can act as a“lubricant”for ion diffusion between the MXene layers,which significantly improves the ion transfer rate of supercapacitors.The dodecaborate/MXene composites can achieve an extremely high specific capacitance of 366 F.g^-1 at a scan rate of 2 mV.s^-1,which is more than eight times higher than that of MXene(43 F1-)at the same scan rate.Our finding provides a novel route on the fabrication of the high performance supercapacitors.

A parallel data generator for efficiently generating “realistic” social streams

A social stream refers to the data stream that records a series of social entities and the dynamic interac-tions between two entities. It can be employed to model the changes of entity states in numerous applications. The social streams, the combination of graph and streaming data, pose great challenge to efficient analytical query processing, and are key to better understanding users' behavior. Considering of privacy and other related issues, a social stream genera-tor is of great significance. A framework of synthetic social stream generator (SSG) is proposed in this paper. The gener-ated social streams using SSG can be tuned to capture sev-eral kinds of fundamental social stream properties, includ-ing patterns about users' behavior and graph patterns. Ex-tensive empirical studies with several real-life social stream data sets show that SSG can produce data that better fit to real data. It is also confirmed that SSG can generate social stream data continuously with stable throughput and memory consumption. Furthermore, we propose a parallel implemen-tation of SSG with the help of asynchronized parallel pro-cessing model and delayed update strategy. Our experiments verify that the throughput of the parallel implementation can increase linearly by increasing nodes.