The incorporation of oxygen vacancies in anatase TiO has been studied as a promising way to accelerate the transport of electrons and Na ions, which is important for achieving excellent electrochemical properties for anatase TiO. However, wittingly introducing oxygen vacancies in anatase TiO for sodium-ion anodes by a facile and effective method is still a challenge. In this work, we report an innovative method to introduce oxygen vacancies into the urchin-like N-doped carbon coated anatase TiO (NC-DTO) by a facile plasma treatment. The superiorities of the oxygen vacancies combined with the conductive N-doped carbon coating enable the obtained NC-DTO of greatly improved sodium storage performance. When served as the anode for sodium-ion batteries, the NC-DTO electrode shows superior electrochemical performance (capacity: 272 mA h g at 0.25 C, capacity retention: 98.8% after 5000 cycles at 10 C, as well as ultrahigh capacity: 150 mA h g at 15 C). Density functional theory calculations combined with experimental results suggest that considerably improved sodium storage performance of NC-DTO is due to the enhanced electronic conductivity from the N-doped carbon layer as well as narrowed band gap and lowered sodiation energy barrier from the introduction of oxygen vacancies. This work highlights that introducing oxygen vacancies into TiO by plasma is a promising method to enhance the electrochemical property of TiO, which also can be applied to different metal oxides for energy storage devices.

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We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the