The electrochemical properties of Li3V2(PO4)3 (LVP) cathode of lithium ion batteries are often improved by ion doping. Nevertheless, the mechanism of ion doping has not been fully understood. Here, Ti4+ has been chosen as a typical dopant with similar atomic radius to the six-coordinated V3+. A series of Li3Ti x V(2–x)(PO4)3/C samples are successfully synthesized by a sol–gel route. The 7Li MAS NMR spectra of the LT x VP/C demonstrate that the doping of Ti4+ can enhance the mobility of Li ions. The results of electrochemical properties tests show that moderate Ti4+ doping is able to improve the high rate capability of the materials by increasing the electronic conductivity and Li-ion diffusion coefficient. The optimal sample (LT0.08VP/C) exhibits the best cycling behavior and rate capability, which can deliver 110.85 mAh/g and capacity retention of 99.36% at 10 C after 100 cycles. Electrochemical impedance spectroscopy results indicate that LT0.08VP/C possesses the minimum charge transfer resistance. The calculation results of cyclic voltammetry illustrate that the Li-ion diffusion coefficient of LT0.08VP/C has been improved. By combining the information extracted from a series of electrochemical characterizations and NMR tests, a structural model of Li+ vacancy is proposed to explain the improving of Li+ mobility.

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Monitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.