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The exploitation of noble-metal-free photocatalysts with high solar-to-H2 conversion efficiency is a hot topic in the photocatalysis field. Molybdenum sulfide materials, which have good physicochemical properties and excellent hydrogen evolution activity, have become an effective noble metal cocatalyst substitute and attracted widespread attention. In this work, a highly efficient photocatalyst constructed by decorating thiomolybdate [Mo2S12]2- nanoclusters on TiO2 is reported for the first time. The resultant [Mo2S12]2-/TiO2 photocatalyst shows a remarkable enhanced hydrogen evolution rate under the Xenon light irradiation. At the optimal loading amount of [Mo2S12]2-, the photocatalyst exhibits a photocatalytic hydrogen evolution rate of 213.1 μmol h−1 g−1, which is about 51 times that of the pure TiO2. Characterization results show that the intimate contact between [Mo2S12]2- and TiO2 promotes the separation of hole-electron pairs, prolongs the lifetime of carriers, and thereby increases the photocatalytic activity. Furthermore, abundant bridging S in the [Mo2S12]2- acts as active sites for hydrogen evolution, which also contributes to the enhanced hydrogen production rate. This work demonstrates an efficient way for the construction of noble-metal-free hydrogen evolution photocatalyst and provides a useful reference for the development of low cost photocatalysts in the future.

Objective To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists. Materials and methods A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I). Results The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time. Conclusions Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Aqueous zinc-ion batteries (ZIBs) have received considerable attention for reliable and low-cost energy storage. However, it remains a great challenge to develop cathode materials with high capacity and adequate cycle life due to the high polarization of bivalent Zn2+. Defect engineering has been demonstrated to enhance the electrochemical reaction sites but most defects are restricted to the surface of materials. We overcome this issue by incorporation of defect engineering and nanoengineering. Oxygen-defective V2O5 nanosheet arrays anchored on carbon cloth are employed as the cathode of ZIBs, which delivering comparable reversible capacity (322.9 and 256.6 at 1 and 5 A g−1) and stable cyclability (220 mAh g−1 after 500 cycles at 10 A g−1). Further DFT calculations validate that Zn2+ diffusion in oxygen-defective V2O5 is allowed along c axis, not only restricted along ab plane of V2O5, thus realizing a 3D Zn2+ diffusion with fast electrochemical kinetics and large capacitive storage. Moreover, the Zn2+ adsorption energies at defective sites of V2O5 was close to thermoneutral value, contributing reversible Zn2+ adsorption/desorption for ultra-stable Zn2+ storage performance. This synergistic strategy of defect engineering and nanoengineering reveal a promising potential for advanced materials of aqueous Zn-ion batteries and flexible storage applications.

Purpose To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer. Method The dataset consisted of 1177 samples (benign/malignant: 414/763) from 997 patients provided by collaborating hospital. All nodules were biopsy or surgery proven, and pathologic results were regarded as the “golden standard”. This study utilized partial U-Net to capture the low-level (edge, corner, etc.) information and CapNets to preserve high-level (semantic information) information of nodules. For CapNets, each capsule had a 4 × 4 matrix representing the pose and an activation probability representing the presence of an object. Furthermore, we chose accuracy (ACC), area under the curve (AUC), sensitivity (SE) and specificity (SP) to evaluate the generalization of the proposed architecture and compared its identification performance with 3D U-Net and experienced radiologists. Results The AUC of our architecture (0.84) was superior to that (0.81) of the original 3D U-Net (p = 0.04, DeLong’s test). Moreover, ACC (84.5 %) and SE (92.9 %) of our model were clearly higher than radiologists’ ACC (81.0 %) and SE (84.3 %) at the optimal operating point. However, SP (70 %) of our model was slightly lower than radiologists’ SP (75 %), which might be the result of class imbalance with limited benign samples involved for algorithm training. Conclusions Our architecture showed a high performance for identifying benign and malignant pulmonary nodules, indicating the improved model has a promising application in clinic.