A Simple and Effective Physical Ball-Milling Strategy to Prepare Super-Tough and Stretchable PVA@MXene@PPy Hydrogel for Flexible Capacitive Electronics.

Biomimetic flexible electronics for E-skin have received increasing attention, due to their ability to sense various movements. However, the development of smart skin-mimic material remains a challenge. Here, a simple and effective approach is reported to fabricate super-tough, stretchable, and self-healing conductive hydrogel consisting of polyvinyl alcohol (PVA), Ti3 C2 Tx MXene nanosheets, and polypyrrole (PPy) (PMP hydrogel). The MXene nanosheets and Fe3+ serve as multifunctional cross-linkers and effective stress transfer centers, to facilitate a considerable high conductivity, super toughness, and ultra-high stretchability (elongation up to 4300%) for the PMP hydrogel with. The hydrogels also exhibit rapid self-healing and repeatable self-adhesive capacity because of the presence of dynamic borate ester bond. The flexible capacitive strain sensor made by PMP hydrogel shows a relatively broad range of strain sensing (up to 400%), with a self-healing feature. The sensor can precisely monitor various human physiological signals, including joint movements, facial expressions, and pulse waves. The PMP hydrogel-based supercapacitor is demonstrated with a high capacitance retention of ≈92.83% and a coulombic efficiency of ≈100%.

Deep learning for predicting refractive error from multiple photorefraction images.

BACKGROUND: Refractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN). It not only extracts the features of each image, but also fully utilizes the sequential relationship between images. In this article, we develop a system to predict the spherical power, cylindrical power, and spherical equivalent in multiple eccentric photorefraction images. Approach First, images of the pupil area are extracted from multiple eccentric photorefraction images; then, the features of each pupil image are extracted using the REDNet convolution layers. Finally, the features are fused by the recurrent layers in REDNet to predict the spherical power, cylindrical power, and spherical equivalent. RESULTS: The results show that the mean absolute error (MAE) values of the spherical power, cylindrical power, and spherical equivalent can reach 0.1740 D (diopters), 0.0702 D, and 0.1835 D, respectively. SIGNIFICANCE: This method demonstrates a much higher accuracy than those of current state-of-the-art deep-learning methods. Moreover, it is effective and practical.