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Sodium ions have been successfully doped into LiNi0.8Co0.1Mn0.1O2 (NCM811) by solid phase method. The influences of Na doping on the electrochemical properties of the electrode at high current intensities were studied. The experimental results show that 3% Na modified LiNi0.8Co0.1Mn0.1O2 has superior rate performance and cycling behavior due to the cation mixing is suppressed and the structural stability is enhanced by Na doping. The capacity retention of Li0.97Na0.03Ni0.8Co0.1Mn0.1O2 is 64.33% after 100th cycles at 10 C (1 C = 200 mA · g–1) in the potential range from 2.8 V to 4.3 V, which is greatly higher than the pristine NCM (only 35.01%). Li0.97Na0.03Ni0.8Co0.1Mn0.1O2 also shows excellent rate performance of 140.5 mAh · g–1 at 10 C, and the discharge capacity of 3% Na-NCM is higher than that of the pristine NCM when the current intensity is returned from 10 C to 0.5 C. The analysis of the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements confirm that the Na doping plays an important role in inhibiting the electrochemical polarization of LiNi0.8Co0.1Mn0.1O2 and improving the diffusion of lithium ions.

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Activated carbon materials are used in hybrid battery capacitors. They reduce energy density of devices, and can greatly improve the cycle life and power density. Herein, we used fast-growing persimmon branches in Shaanxi as a biomass carbon source. The persimmon branch activated carbon (PB-AC850) material was found to exhibit abundant graded pore structure similar to graphite structure after KOH activation. The macro/mesoporous structure in PB-AC850 facilitated the ions (solvated PF-6 ) transport, resulting in much better rate capability as compared to commercial activated carbon. It was moreover found from preparation of the hybrid battery capacitor that the addition of activated carbon reduced resistance and polarization of the device. LFP+PB-AC850 exhibited excellent cycle stability with high to 93.4% capacity reservation after 500 cycles at 5C.

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Since the reform began in our country, with the rapid economic growth in recent years, the income level has grown extremely unequal, and it is difficult for the low-income poor to benefit from the rapid economic growth. The most important prerequisite for the fight against poverty is the accurate identification of the causes of poverty. To date, our country has not reached the level of maturity required to accurately study the causes of poverty in various households. However, with the rapid development of Internet technology and big data technology in recent years, the application of large-scale data technology and data extraction algorithms to poverty reduction can identify truly poor households faster and more accurately. Compared with traditional machine learning algorithms, there are no machine storage and technical constraints, can use a large amount of data and rely on multiple data samples.