RESUMO
The high-entropy materials (HEM) have attracted increasing attention in catalysis and energy storage due to their large configurational entropy and multiunique properties. However, it is failed in alloying-type anode due to their Li-inactive transition-metal compositions. Herein, inspired by high-entropy concept, the Li-active elements instead of transition-metal ones are introduced for metal-phosphorus synthesis. Interestingly, a new Znx Gey Cuz Siw P2 solid solution is successfully synthesized as proof of concept, which is first verified to cubic system in F-43m. More specially, such Znx Gey Cuz Siw P2 possesses wide-range tunable region from 9911 to 4466, in which the Zn0.5 Ge0.5 Cu0.5 Si0.5 P2 accounts for the highest configurational entropy. When served as anode, Znx Gey Cuz Siw P2 delivers large capacity (>1500 mAh g-1 ) and suitable plateau (≈0.5 V) for energy storage, breaking the conventional view that HEM is helpless for alloying anode due to its transition-metal compositions. Among them, the Zn0.5 Ge0.5 Cu0.5 Si0.5 P2 exhibits the highest initial coulombic efficiency (ICE) (93%), Li-diffusivity (1.11 × 10-10 ), lowest volume-expansion (34.5%), and best rate performances (551 mAh g-1 at 6400 mA g-1 ) owing to its largest configurational entropy. Possible mechanism reveals the high entropy stabilization enables good accommodation of volume change and fast electronic transportation, thus supporting superior cyclability and rate performances. This large configurational entropy strategy in metal-phosphorus solid solution may open new avenues to develop other high-entropy materials for advanced energy storage.
RESUMO
BACKGROUND: Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs. METHODS: A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model's performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists. RESULTS: The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists' readings. CONCLUSIONS: The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists' workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.
