Tianjun Wang

An asymmetric memristive device fabricated with a titania (TiO_x)-based switching layer deposited through atomic layer deposition with a thickness of ∼37 nm was investigated. X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy coupled with energy-dispersive x-ray spectroscopy were employed for device structural characterization. A unipolar resistive switching behavior (both at positive and negative voltages) was observed through the memristor’s current–voltage characteristics. A remarkably smaller forming voltage (from the top Pt electrode to the grounded Au electrode) of 0.46 V was achieved, while it approached (positive bias from the Au electrode and holding Pt electrode as grounded) 0.25 V, which is a much smaller forming voltage than has ever been reported for titanium-based oxides without doping. The retention and endurance characterization over 2000 switching cycles were satisfactory without degradation.

Lead-free perovskite is one of the ideal solutions for the toxicity and instability of lead halide perovskite quantum dots. As the most ideal lead-free perovskite at present, bismuth-based perovskite quantum dots still have the problem of a low photoluminescence quantum yield, and its biocompatibility also needs to be explored. In this paper, Ce³⁺ ions were successfully introduced into the Cs₃Bi₂Cl₉ lattice using a modified antisolvent method. The photoluminescence quantum yield of Cs₃Bi₂Cl₉:Ce is up to 22.12%, which is 71% higher than that of undoped Cs₃Bi₂Cl₉. The two quantum dots show high water-soluble stability and good biocompatibility. Under the excitation of a 750 nm femtosecond laser, high-intensity up-conversion fluorescence images of human liver hepatocellular carcinoma cells cultured with the quantum dots were obtained, and the fluorescence of the two quantum dots was observed in the image of the nucleus. The fluorescence intensity of cells cultured with Cs₃Bi₂Cl₉:Ce was 3.20 times of that of the control group and 4.54 times of the control group for the fluorescence intensity of the nucleus, respectively. This paper provides a new strategy to develop the biocompatibility and water stability of perovskite and expands the application of perovskite in the field.

Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.