In this study, well dispersed Cu@Co core–shell nanoparticles (NPs) on rGO surfaces were successfully synthesized via a one-step in situ procedure using methylamine borane (MeAB) as reductant under ambient conditions. The Cu@Co/rGO NPs exhibit superior catalytic activity than their alloy (CuCo/rGO) and graphene-free (Cu@Co) counterparts toward the hydrolytic dehydrogenation of ammonia borane (AB). Additionally, compared with the NPs reduced by AB, the as-synthesized Cu@Co/rGO NPs generated by the weaker reducing agent MeAB exhibit higher catalytic activities. Furthermore, the as-synthesized NPs exerted satisfactory catalytic activities and recycle stabilities for the hydrolysis of MeAB. Moreover, this general method indicates that MeAB can be used as both a potential hydrogen storage material and an efficient reductant which can be easily extended to the facile preparation of other rGO-supported metal NPs.

Transition metal phosphating Pd@NiP nanoparticles supported by reduced graphene oxide were successfully synthesized by a one-step in situ synthesis method. The Pd@NiP/rGO nanoparticles were characterized by powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), confocal Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FT-IR), and BET. Compared with Pd/rGO and Pd@Ni/rGO, Pd₁@Ni_12.5P₄₀/rGO exhibited stronger catalytic activity, with TOF value reaching 133.33min^-1 and activation energy (Ea) of 29.31kJmol^-1. This excellent catalytic performance may be related to the good dispersion of nanoparticles and the synergistic electron interactions between transition metal phosphating compounds.

This work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users’ charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users’ charging choices can effectively decrease the scheduling deviation caused by users’ charging choices. It greatly improves the security and economy of the grid.