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Three-dimensional (3D) graphene has attracted increasing attention in electrochemical devices. However, the existing preparation technologies usually involve a solvent process, which introduces defects and functional groups into the 3D network. Here, we find the defects and functional groups influence the electrochemical stability of graphene. After an electrochemical process, the current decreases by more than 1 order of magnitude, indicating remarkable etching of graphene. To improve the electrochemical stability, we develop a solvent-free preparation process to produce 3D graphene for the first time. After growth on a 3D microporous copper by chemical vapor deposition (CVD), the copper template is removed by a high temperature evaporation process, resulting in 3D graphene network without any solvent process involved. The samples exhibit remarkably improved stability with durable time 2 times, compared with normal CVD samples, and 55 times, compared with reduced graphite oxide, and no obvious etching is observed at 1.6 V versus saturated calomel electrode, showing great potential for application in future 3D graphene-based high stable electrochemical devices.

BackgroundDevelopmental dysplasia of the hip (DDH) is a common orthopedic disease in children. In clinical surgery, it is essential to quickly and accurately locate the exact position of the lesion, and there are still some controversies relating to DDH status. We adopt artificial intelligence (AI) to solve the above problems.MethodsIn this paper, automatic DDH measurements and classifications were achieved using a three-stage pipeline. In the first stage, we used Mask-RCNN to detect the local features of the image and segment the bony pelvis, including the ilium, pubis, ischium, and femoral heads. For the second stage, local image patches focused on semantically related areas for DDH landmarks were extracted by high-resolution network (HRNet). In the third stage, some radiographic results are obtained. In the above process, we used 1,265 patient x-ray samples as the training set and 133 samples from two other medical institutions as the verification set. The results of AI were compared with three orthopedic surgeons for reliability and time consumption.ResultsAI-aided diagnostic system's Tönnis and International Hip Dysplasia Institute (IHDI) classification accuracies for both hips ranged from 0.86 to 0.95. The measurements of numerical indices showed that there was no statistically significant difference between surgeons and AI. Tönnis and IHDI indicators were similar across the AI system, intermediate surgeon, and junior surgeon. Among some objective interpretation indicators, such as acetabular index and CE angle, there were good stability and consistency among the four observers. Intraclass consistency of acetabular index and CE angle among surgeons was 0.79–0.98, while AI was 1.00. The measurement time required by AI was significantly less than that of the doctors.ConclusionThe AI-aided diagnosis system can quickly and automatically measure important parameters and improve the quality of clinical diagnosis and screening referral process with a convenient and efficient way.

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