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Owing to the properties of cost-efficient, high-energy density and environmental friendliness, rechargeable aqueous zincmetal batteries (RAZMBs) are promising candidates for the next generation metal-based batteries in large-scale energy storage systems. However, the practical applications of RAZMBs are severely limited due to the presence of inevitable side reactions referring to zinc corrosion and hydrogen evolution reaction (HER) occurring at the electrode-electrolyte interface. The uninterrupted interfacial side reactions at the expense of continuous capacity fading and reduced reversibility of zinc are required to give more attention to mitigate them. Given the above concerns, in this review, the fundamental principles of corrosion thermodynamics and kinetics of zinc electrodes in aqueous media are elucidated. Furthermore, the recent optimization strategies targeting enhanced stability of zinc electrodes are reviewed including electrolyte additives, zinc alloying, electrodeposited Zn and coating treatments. Finally, considering the current main research orientations, some perspectives are provided to facilitate the development of future applications of zinc anodes.
Recent research has reported the great influence of springtime land surface temperature (LST) and subsurface temperature (SUBT) over the Tibetan Plateau (TP) on downstream region summer precipitation, indicating the potential application of LST/SUBT on subseasonal to seasonal (S2S) prediction. In this study, we employed both observational data and offline model simulation to explore the memory of surface and subsurface variables and assess the driving effects of snow/albedo and SUBT on the LST anomaly. Our composite analysis based on observation shows that the anomalous LST in the TP can sustain for seasons and is accompanied by persistent SUBT as well as snow and associated surface albedo anomalies. A multilayer frozen soil model reproduces the observed LST anomaly and shows surface albedo and middle-layer SUBT have 1-3 months memory, indicating the degree of persistence or dissipation of anomaly through time with more extended memory during spring. With simulated middle-layer SUBT as a predictor, the linear regression model produces R 2 adj of 0.44 and 0.26 for 1-and 2-month LST prediction, respectively. The predictability is higher during the spring. Our results also show February snowfall, May snowmelt, and aero in snow exert substantial impacts on springtime LST through snow albedo feedback. The long memory of SUBT allows it to preserve the surface thermal anomaly and release it gradually in the following months to seasons. Meanwhile, the sensitivity study indicates that the soil properties and soil column depth predominate SUBT memory, which suggests the key processes to improve LST/SUBT then downstream S2S prediction.
Temperature drift is the main source of errors affecting the precision and performance of a dynamically tuned gyroscope (DTG). In this paper, the support vector machine (SVM), a novel learning machine based on statistical learning theory (SLT), is described and applied in the temperature drift modelling and compensation to reduce the influence of temperature variation on the output of the DTG and to enhance its precision. To improve the modelling and compensation capability, wavelet transform (WT) is introduced into the SVM model to eliminate any impactive noises. The real temperature drift data set from the long-term measurement system of a certain DTG is employed to validate the effectiveness of the proposed combination strategy. Moreover, the traditional neural network (NN) approach is also investigated as a comparison with the SVM based method. The modelling and compensation results indicate that the proposed WT-SVM model outperforms the NN and single SVM models, and is feasible and effective in temperature drift modelling and compensation of the DTG.
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