RESUMO
Recently, ethanol has shown promising potential in the large-scale reduction of graphene oxide (GO) into graphene. However, dispersion of GO powder in ethanol is a challenge due to its poor affinity, which hinders permeation and intercalation of ethanol between GO molecule layers. In this paper, phenyl-modified colloidal silica nanospheres (PSNS) were synthesized by phenyl-tri-ethoxy-silane (PTES) and tetra-ethyl ortho-silicate (TEOS) using a sol-gel method. PSNS was then assembled onto a GO surface to form a PSNS@GO structure by possible non-covalent π-π stacking interactions between the phenyl groups and GO molecules. The surface morphology, chemical composition, and dispersion stability were analyzed by scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetry, Raman spectroscopy, X-ray diffractometry, nuclear magnetic resonance, and particle sedimentation test. The results showed that the as-assembled PSNS@GO suspension had excellent dispersion stability with an optimal PSNS concentration of 5 vol% PTES. With the optimized PSNS@GO, ethanol can permeate between the GO layers and intercalate along with PSNS particles via formation of hydrogen bonds between assembled PSNS on GO and ethanol, achieving a stable dispersion of GO in ethanol. The optimized PSNS@GO powder remained redispersible after drying and milling according to this interaction mechanism which is favorable for large scale reduction processes. Higher PTES concentration may result in agglomeration of PSNS and formation of wrapping structures of PSNS@GO after drying and worsen its dispersion capability.
RESUMO
A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles, this study proposes a continuous multiple scanning recursive matching algorithm based on a single-line LIDAR sensor. Radar recursive scanning is used to obtain the multiple superposition data of echo signals, and coordinate matching is realized between historical scanning data and current scanning data. Simultaneously, the sensor height deviation and pitch angle deviation of the sensors are regressed to obtain an accurate pavement elevation. Considering the control effect of the active vehicle suspension, a vehicle suspension model with seven degrees of freedom is established. The semi-active suspension controller is constructed using a diagonal recursive neural network algorithm, and the neural network weight is trained using a genetic algorithm. In addition, a preview diagonal recursive neural network control strategy for semi-active suspension, based on the combination of road elevation information, is proposed. The results of a hardware-in-the-loop co-simulation, which was conducted based on the Simulink control model and dSPACE real-time simulation, revealed that the ride comfort and stability of the vehicle were improved owing to a preview of the elevation information of the road ahead and the active adjustment of the shock absorber of the suspension system.
