A dual-confinement strategy to construct cobalt-based phosphide nanoclusters within carbon nanofibers for bifunctional water splitting electrocatalysts.

It is a challenging task to explore highly active and stable noble-metal-free bifunctional electrocatalysts for water splitting, both in hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Herein, a new dual-confinement strategy for the fabrication of cobalt-base phosphide in the carbon nanofibers (CNFs) was proposed via electrospinning, followed by the corresponding pyrolysis. The ultrafine phosphides derived from the pore confinement of ZIF and space confinement of the polymer revealed abundant active sites and P defects. More importantly, by introducing a second metal element Ni or Cu, the electronic structure and synergistic effect were further enhanced, and the obtained bimetallic CoNiPx-CNF electrocatalyst exhibited the remarkable performance for HER and OER, featuring the low η10 values of 154 and 269 mV in 1.0 M KOH electrolyte, respectively. CoNiPx-CNFs as a catalyst for both anode and cathode showed a current density of 10 mA cm-2 at a voltage of 1.56 V, exceeding better stability, which is superior to most non-noble metal electrocatalysts reported in a previous research. The dual-confinement strategy is believed to provide an effective and simple approach for the synthesis of high-performance and cost-efficient bifunctional electrocatalysts for overall water splitting.

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.

Synthesis of the double-shell anatase-rutile TiO2 hollow spheres with enhanced photocatalytic activity.

A novel double-shell TiO2 hollow sphere with an inner anatase shell and an outer rutile shell was synthesized by a simple sol-gel method and silica protected calcination process. The structure and formation mechanism was proposed based on characterization using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The double-shell spheres have a uniform diameter of 360 nm and a typical yolk-shell structure. Moreover, the double-shell TiO2 hollow spheres possess a large specific surface area (169 m(2) g(-1)). Due to the high surface area, multiple light reflection and beneficial electron conduction between the inner anatase and outer rutile shell of this special structure, the as-prepared double-shell TiO2 catalysts show remarkably enhanced photoactivity compared to the commercial P25 catalyst. In particular, rhodamine B molecules can be completely decomposed in the presence of the double-shell spheres after 60 minutes of irradiation with UV light. In addition, the high activity is retained after five cycles, indicating the stability and reusability of the double-shell catalyst.