Based on the structural characteristics of the anodes of lithium-ion batteries, an improved Hummers’ method is proposed to recycle the anode materials of spent lithium-ion batteries into graphene. In order to effectively separate the active material from the copper foil, water was selected as an ultrasonic solvent in this experiment. In order to further verify whether lithium ions exist in the active material, carbon powder, it was digested by microwave digestion. ICP-AES was then used to analyse the solution. It was found that lithium ions were almost non-existent in the carbon powder. In order to further increase the added value of the active material, graphene oxide was obtained by an improved Hummers’ method using the carbon powder. The graphene material was also reduced by adding vitamin C as a reducing agent through a chemical reduction method using graphene oxide. Meanwhile, the negative graphite, graphite oxide and graphene samples were characterized by XRD, SEM, FTIR and TEM. The conductivity of the negative graphite, graphite oxide and graphene was tested. The results show that graphene prepared by a redox method has a better layered structure, less impurities and oxygen groups in its molecular structure, wider interlayer spacing and smaller resistivity.

A uric acid (UA) electrochemical biosensor was constructed using ferrocene (fc) decorated cuprous oxide (cu 2 o) enhanced electro-active characteristics and covalently immobilized with uricase (Uox) on glassy carbon electrode (Gce). the electrochemical characteristics of the fabricated electrode was analysed by cyclic voltammetry, electrochemical impedance spectroscopy and differential pulse voltammetry (DpV). DpV studies revealed rapid response of fabricated electrode Uox/fc/cu 2 o/Gce towards UA in a wide concentration range of 0.1-1,000 μM with a sensitivity of 1.900 μA mM −1 cm −2 and very low detection limit of 0.0596 μM. A very low magnitude Michaelis-Menten constant (Km) value was evaluated as 34.7351 μM which indicated the chemical attraction of the enzyme towards the UA was much higher. the developed biosensor was successfully applied to detect UA in human urine samples. Moreover, reproducibility and stability studies demonstrated the fabricated UOx/Fc/ cu 2 O/GCE biosensor had high reproducibility with a RSD of 2.8% and good reusability with a RSD of 3.2%. Specificity studies results showed the fabricated biosensor had strong anti-interference ability. the improved sensor performance was attributed to the synergistic electronic properties of cu 2 o and fc that provided enhances delectrocatalytic activity and electron transfer. the present biosensor can be extended for use in clinical settings.

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This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

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