Aqueous rechargeable zinc ion batteries are promising candidates for grid-scale applications owing to their low cost and high safety. However, they are plagued by the lack of suitable cathode and anode materials. Herein, we report on potassium vanadate (KVO) nanobelts as a promising cathode for an aqueous zinc ion battery, which shows a high discharge capacity of 461 mA h g −1 at 0.2 A g −1 and exhibits a capacity retention of 96.2% over 4000 cycles at 10 A g −1 . Furthermore, to enhance the energy efficiency in an aqueous zinc ion battery, a facile and effective method on the anode is demonstrated. The energy efficiency increases from 47.5% for Zn//KVO coupled with the zinc foil anode to 66.5% for AB-Zn//KVO coupled with an acetylene black film improved zinc foil anode at 10 A g −1 . The remarkable electrochemical performance makes AB-Zn//KVO a strong candidate for a high-performance aqueous zinc ion battery.

The effect of different annealing temperatures on the electrochemical performance of potassium ammonium vanadate (KNVO) was investigated, and the annealed KNVO regained H2O from the aqueous electrolyte to achieve an...

Depression (DP) and schizophrenia (SCZ) are both highly prevalent psychiatric disorders, and their diagnosis depends on the examination of symptoms and clinical tests, which can be subjective. As a measure of real-time neural activity, Electroencephalographic (EEG) has shown its usability to classify people either as normal or as having DP or SCZ, but automatic classification between the three categories (DP, SCZ and the normal) was rarely reported. Here, we propose an automatic diagnostic framework based on a convolutional neural network called the Multi-Channel Frequency Network (MUCHf-Net), which automatically learns feature representations of EEGs that characterize them as normal, DP, or SCZ. Two EEG databases were used in this study, the first one contains EEGs from 300 individuals (DP: 100, SCZ: 100, normal: 100) collecting from our hospital, and the second contains EEGs from 30 individuals (DP: 10, SCZ: 10, normal: 10) from public available datasets, and the spectrum matrices from these multi-channel EEGs were feed into MUCHf-Net. The results showed that: (1) MUCHf-Net accurately distinguished normal EEGs from DP or SCZ EEGs (accuracy: 91.12%; F1 score: 0.8947); (2) low-frequency bands (delta, theta, alpha) contributed the most important information to the classification model; (3) features located in the frontal and parietal lobes contributed more than other regions did; (4) MUCHf-Net fine-tuned on public datasets also had high classification accuracy: 87.71% (triple: normal, SCZ or DP) and 79.27% (binary: psychiatric disorders (DP or SCZ) or normal). Our study shows that deep leaning has the potential to become an important tool for assisting in the diagnosis of psychiatric disorders.

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Si-Cu alloys have been prepared by electroless depositions with different process. Si-Cu/MCMB materials have been prepared by carbonization of the mixture of Si-Cu, MCMB and mesophase pitch. The influences of electroless deposition condition and heat-treatment temperature on the anodic performance of Cu-Si/MCMB have been studied. Compared with Si/MCMB, Si-Cu/MCMB has more stable cyclical performance. Furthermore, the Si-Cu/MCMB sample produced by cleaning and then electroless deposition on Si shows the better anodic performance than the Si-Cu/MCMB sample produced by direct electroless deposition on Si, because the former sample has more Cu contents than the latter sample.