Asymmetric capacitors based on TiO<Subscript>2</Subscript> and mesoporous MnO<Subscript>2</Subscript> electrodes using neutral aqueous electrolyte

Asymmetric capacitor based on TiO₂ with the size range from 90 to 410 nm and mesoporous MnO₂ (ca. 200–380 nm) electrodes has been successfully constructed and characterized in LiClO₄ aqueous electrolyte. The samples of both metal oxides were fully characterized by scanning electron microscopy (SEM), X-ray powder diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), N₂ adsorption-desorption, and so on. The electrochemical capacitive performances of both electrode materials were evaluated by cyclic voltammetry and galvanostatic charge-discharge in 1 mol/L LiClO₄ with a working voltage of 2.0 V. The discharge profile of the asymmetric capacitor exhibited an excellent capacitive behavior and good cycling stability after 2000 cycles. Moreover, the TiO₂//MnO₂ asymmetric capacitor possesses both higher energy density and power density (7.7 Wh/kg, 762.5 W/kg) than that of Maxsorb//Maxsorb symmetrical capacitor (7.0 Wh/kg, 400.0 W/kg).

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Graphical abstract A novel asymmetric capacitor based on TiO₂ and mesoporous MnO₂ electrodes has been successfully constructed and characterized in LiClO₄ aqueous electrolyte.

     

A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding

With the development of technology and the rise of the meta-universe concept, the brain-computer interface (BCI) has become a hotspot in the research field, and the BCI based on motor imagery (MI) EEG has been widely concerned. However, in the process of MI-EEG decoding, the performance of the decoding model needs to be improved. At present, most MI-EEG decoding methods based on deep learning cannot make full use of the temporal and frequency features of EEG data, which leads to a low accuracy of MI-EEG decoding. To address this issue, this paper proposes a two-branch convolutional neural network (TBTF-CNN) that can simultaneously learn the temporal and frequency features of EEG data. The structure of EEG data is reconstructed to simplify the spatio-temporal convolution process of CNN, and continuous wavelet transform is used to express the time-frequency features of EEG data. TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. The experimental results on the BCI competition IV 2b dataset show that the proposed model achieves an average classification accuracy of 81.3% and a kappa value of 0.63. Compared with other methods, TBTF-CNN achieves a better performance in MI-EEG decoding. The proposed method can make full use of the temporal and frequency features of EEG data and can improve the decoding accuracy of MI-EEG.     

Porous cobalt oxide (Co3O4) nanorods: Facile syntheses, optical property and application in lithium-ion batteries

We developed a facile synthetic route of porous cobalt oxide (Co₃O₄) nanorods via a microemulsion-based method in combination with subsequent calcination process. The porous structure was formed by controlled decomposition of the microemulsion-synthesized precursor CoC₂O₄ nanorods without destruction of the original morphology. The as-prepared Co₃O₄ nanorods, consisting of small nanoparticles with diameter of 80–150 nm, had an average diameter of 200 nm and a length of 3–5 μm. The morphology and structure of synthesized samples were characterized by transmission electron microscopy and scanning electron microscopy. The phase and composition were investigated by X-ray powder diffraction and X-ray photoelectron spectroscopy. The optical property of Co₃O₄ nanorods was investigated. Moreover, the porous Co₃O₄ nanorods exhibited high electrochemical performance when applied as cathode materials for lithium-ion batteries, which gives them good potential applications.