Ionic actuators have attracted attention due to their remarkably large strain under low-voltage stimulation. Because actuation performance is mainly dominated by the electrochemical and electromechanical processes of the electrode layer, the electrode material and structure are crucial. Here, we report a graphitic carbon nitride nanosheet electrode-based ionic actuator that displays high electrochemical activity and electromechanical conversion abilities, including large specific capacitance (259.4 F g−1) with ionic liquid as the electrolyte, fast actuation response (0.5±0.03% in 300 ms), large electromechanical strain (0.93±0.03%) and high actuation stability (100,000 cycles) under 3 V. The key to the high performance lies in the hierarchical pore structure with dominant size <2 nm, optimal pyridinic nitrogen active sites (6.78%) and effective conductivity (382 S m−1) of the electrode. Our study represents an important step towards artificial muscle technology in which heteroatom modulation in electrodes plays an important role in promoting electrochemical actuation performance.

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Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

The char of three typical biomasses, rice straw char (RS char), chinar leaves char (CL char), and pine sawdust char (PS char), was prepared in a high-frequency furnace, which could efficiently reduce secondary reactions under rapid pyrolysis conditions at 800−1200 °C. The rapid pyrolysis char produced was isothermally gasified in a thermogravimetric analyzer (TGA) under a CO2 atmosphere. Effects of biomass type and pyrolysis temperature on intrinsic carbon structures and morphologic structures of char and further on gasification characteristics of char were investigated using a Raman spectrum analyzer, scanning electron microscopy (SEM), and a surface area and pore size distribution analyzer. Gasification kinetic models were also contrastively discussed under different conditions. Results show that gasification rates decrease with the increasing pyrolysis temperature. Under the morphologic characteristic reserved conditions, morphologic structures present obvious effects on gasification rates. Gasification reactivity of the three biomass chars is in the order of CL char > RS char > PS char. Melting and shrinkage happen during rapid pyrolysis of PS, and the disappearance of the pore and decrease of the specific surface area of PS char lead to the low specific surface area and gasification rates of PS char. Unobvious melting happens to RS char and CL char, and the initial physical structures can be almost reserved, while CL char presents larger porosity and specific surface area, which make its gasification rates higher than those of RS char. In most conditions, the random pore model (RPM) performs well to describe gasification rates of biomass char studied in this work. However, for gasification of PS char at high temperatures, during which high gasification rates can be maintained in high conversion ranges, the modified random pore model (M-RPM) performs better. For gasification of RS char and CL char at low temperatures, during which gasification rates present a sharp decrease and trailing in medium−high conversion ranges, the shifted M-RPM performs better.