An in situ phase transformation induced mesoporous heterointerface for the alkaline hydrogen evolution reaction.

The phase structure of a catalyst plays a crucial role in determining the catalytic activity. In this study, a facile phosphorization process is employed to achieve the in situ phase transformation from single-phase Co3O4 to CoO/CoP hybrid phases. Characterization techniques, including XRD, BET, SEM, and TEM, confirm the retention of the mesoporous nature during the phase transformation, forming porous CoO/CoP heterointerfaces. Strong charge transfer is observed across the CoO/CoP heterointerface, indicating a robust interaction between the hybrid phases. The CoO/CoP hybrid exhibits significantly enhanced catalytic activity for the alkaline hydrogen evolution reaction (HER) compared to pristine Co3O4. Density Functional Theory (DFT) calculations reveal that the elimination of the band gap in the spin-down band of Co in CoO/CoP contributes to the observed high HER activity. The findings highlight the potential of CoO/CoP hybrids as efficient catalysts for HER, and contribute to the advancement of catalyst design for sustainable energy applications.

Efficient J Peak Detection From Ballistocardiogram Using Lightweight Convolutional Neural Network.

Ballistocardiagram (BCG) is a non-contact and non-invasive technique to obtain physiological information with the potential to monitor Cardio Vascular Disease (CVD) at home. Accurate detection of J-peak is the key to get critical indicators from BCG signals. With the development of deep learning methods, many researches have applied convolution neural network (CNN) and recurrent neural network (RNN) based models in J-peak detection. However, these deep learning methods have limitations in inference speed and model complexity. To improve the computational efficiency and memory utilization, we propose a robust lightweight neural network model, called JwaveNet. Moreover, in the preprocessing stage, J-peaks are re-modeled by a new transformation method based on their physiological meaning, which has been proven to increase performance. In our experiment, BCG signals, including four different sleeping positions, were collected from 24 subjects with synchronous electrocardiogram (ECG) signals. The experiment results have shown that our lightweight model greatly reduces latency and model size compared to other baseline models with high detecting accuracy.

Unobtrusive Inter-beat Interval Estimation from Multichannel Ballistocardiogram Signal Using Kalman Filter.

Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.