Anode interfacial layer formation via reductive ethyl detaching of organic iodide in lithium-oxygen batteries.

As soluble catalysts, redox mediators can reduce the high charging overpotential of lithium-oxygen batteries by providing sufficient liquid-solid interface for lithium peroxide decomposition. However, the redox mediators usually introduce undesirable reactions. In particular, the so-called "shuttle effect" leads to the loss of both the redox mediators and electrical energy efficiency. In this study, an organic compound, triethylsulfonium iodide, is found to act bifunctionally as both a redox mediator and a solid electrolyte interphase-forming agent for lithium-oxygen batteries. During charging, the organic iodide exhibits comparable lithium peroxide-oxidizing capability with inorganic iodides. Meanwhile, it in situ generates an interfacial layer on lithium anode via reductive ethyl detaching and the subsequent oxidation. This layer prevents the lithium anode from reacting with the redox mediators and allows efficient lithium-ion transfer leading to dendrite-free lithium anode. Significantly improved cycling performance has been achieved by the bifunctional organic iodide redox mediator.

Automatic Brain Tumor Segmentation Method Based on Modified Convolutional Neural Network.

In the domain of brain diseases, it is difficult for image registration after some brain structures are severely deformed because of diseases. Fortunately, convolutional neural network have gained many promising results in semantic segmentation challenging tasks in recent years. To enhance the performance of automatic brain tumor segmentation, this paper presents a robust segmentation algorithm based on convolutional neural network, which achieved improvement of 3.84% in segmenting the enhancing tumor. Our network architecture is developed from the prevalent U-Net. We combined it with ResNet and modified it to maximize its performance in our brain tumor segmentation task. In this work, BraTS 2017 dataset was employed to train and test the proposed network. Data imbalance was dealt with using a weighted cross entropy loss function. The problem of overfitting was handled through data augmentation. The proposed method achieved averaged dice scores of 0.883, 0.781 and 0.748 for whole tumor, tumor core and enhancing tumor respectively in the validation set and 0.877, 0.774, 0.757 respectively in the testing set.