Superhydrophilic and Superaerophobic Ru-Loaded NiCo Bimetallic Hydroxide Achieves Efficient Hydrogen Evolution over All pH Ranges.

Realizing an effective, binder-free, and super-wetting electrocatalyst for the hydrogen evolution reaction (HER) at full pH is essential for the creation of clean hydrogen. In this study, the Ru-loaded NiCo bimetallic hydroxide (Ru@NiCo-BH) catalyst was prepared by spontaneous redox reaction. The chemical interaction between Ru NPs and NiCo-BH by the Ru-O-M (M=Ni, Co) interface bond, the electron-rich Ru active site, and the multi-channel nickel foam carrier make the superhydrophilic and superaerophobic surface advantageous for mass transfer in the HER process. Therefore, Ru@NiCo-BH has remarkable HER activity, with low overpotential of 29, 68 and 80 mV, a 10 mA cm-2 current density can be obtained in alkaline, neutral and acidic electrolytes respectively. This work provides a reference for the rational development of universal electrocatalysts for hydrogen evolution in the all pH ranges through simple design strategies.

Aprotic Lithium-Carbon Dioxide Batteries: Reaction Mechanism and Catalyst Design.

In recent years, the combustion of fossil fuels leads to the release of a large amount of CO2 gas, which induces the greenhouse effect and the energy crisis. To solve these problems, researchers have turned their focus to a novel Li-CO2 battery (LCB). LCB has received much attention because of its high theoretical energy density and reversible CO2 reduction/evolution process. So far, the emerging LCB still faces many challenges derived from the slow reaction kinetics of discharge products. In this review, the latest status and progress of LCB, especially the influence of the structure design of cathode catalysts on the battery performance, are systematically elaborated. This review summarizes in detail the existing issues and possible solutions of LCB, which is of high research value for further promoting the development of Li-Air battery.

Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images.

Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification. Usually, to acquire a high-accuracy classification, a specific network architecture needs to be designed for a given classification task. In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps obtained from FEs should be different. The object-specific pooling strategies enable the network to accept arbitrarily sized carotid plaques as input and to capture a more informative context for improving the classification accuracy. Extensive experimental studies on a clinical dataset consisting of 333 subjects with 1332 carotid plaques show the superiority of our OSFP-Net against several state-of-the-art deep learning-based methods. The experimental results demonstrate better clinical agreement between the ground truth and the prediction, which indicates its great potential for use as a risk stratification and as a monitoring tool in the management of patients at risk for stroke.

Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images.

PURPOSE: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel-wall-volume (VWV) using the segmented media-adventitia (MAB) and lumen-intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi-automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction. METHODS: In this paper, we propose a semiautomatic segmentation method based on deep learning to segment the MAB and LIB from carotid three-dimensional ultrasound (3DUS) images. For the MAB segmentation, we convert the segmentation problem to a pixel-by-pixel classification problem. A dynamic convolutional neural network (Dynamic CNN) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine-tuned dynamically in each test task. The LIB is segmented by applying a region-of-interest of carotid images to a U-Net model, which allows the network to be trained end-to-end for pixel-wise classification. RESULTS: A total of 144 3DUS images were used in this development, and a threefold cross-validation technique was used for evaluation of the proposed algorithm. The proposed algorithm-generated accuracy was significantly higher than the previous methods but with less user interactions. Comparing the algorithm segmentation results with manual segmentations by an expert showed that the average Dice similarity coefficients (DSC) were 96.46 ± 2.22% and 92.84 ± 4.46% for the MAB and LIB, respectively, while only an average of 34 s (vs 1.13, 2.8 and 4.4 min in previous methods) was required to segment a 3DUS image. The interobserver experiment indicated that the DSC was 96.14 ± 1.87% between algorithm-generated MAB contours of two observers' initialization. CONCLUSIONS: Our results showed that the proposed carotid plaque segmentation method obtains high accuracy and repeatability with less user interactions, suggesting that the method could be used in clinical practice to measure VWV and monitor the progression and regression of carotid plaques.