Hierarchical structured graphene/metal oxide/porous carbon composites as anode materials for lithium-ion batteries

This work was financially supported by the Fundamental Research Funds for the Central Universities, and National Natural Science Foundation of China (21101014 and 21273022).As a novel anode material for lithium-ion batteries, CeO2 displays imperceptible volumetric and morphological changes during the lithium insertion and extraction processes, and thereby exhibits good cycling stability. However, the low theoretical capacity and poor electronic conductivity of CeO2 hinder its practical application. In contrast, Co3O4 possesses high theoretical capacity, but undergoes huge volume change during cycling. To overcome these issues, CeO2 and Co3O4 nanoparticles are formed inside the pores of CMK-3 and display various electrochemical behaviors due to the different morphological structures of CeO2 and Co3O4 within CMK-3. Moreover, the graphene/metal oxide/CMK-3 composites with a hierarchical structure are then prepared and exhibit better electrochemical performances than metal oxides with or without CMK-3. This novel synthesis strategy is hopefully employed in the electrode materials design for Li-ion batteries or other energy conversion and storage devices.PostprintPeer reviewe

In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems

The remarkable success of the use of machine learning-based solutions for network security problems has been impeded by the developed ML models' inability to maintain efficacy when used in different network environments exhibiting different network behaviors. This issue is commonly referred to as the generalizability problem of ML models. The community has recognized the critical role that training datasets play in this context and has developed various techniques to improve dataset curation to overcome this problem. Unfortunately, these methods are generally ill-suited or even counterproductive in the network security domain, where they often result in unrealistic or poor-quality datasets. To address this issue, we propose an augmented ML pipeline that leverages explainable ML tools to guide the network data collection in an iterative fashion. To ensure the data's realism and quality, we require that the new datasets should be endogenously collected in this iterative process, thus advocating for a gradual removal of data-related problems to improve model generalizability. To realize this capability, we develop a data-collection platform, netUnicorn, that takes inspiration from the classic "hourglass" model and is implemented as its "thin waist" to simplify data collection for different learning problems from diverse network environments. The proposed system decouples data-collection intents from the deployment mechanisms and disaggregates these high-level intents into smaller reusable, self-contained tasks. We demonstrate how netUnicorn simplifies collecting data for different learning problems from multiple network environments and how the proposed iterative data collection improves a model's generalizability

2-Iodo-3-meth­oxy-6-methyl­pyridine

The title compound, C7H8INO, which crystallizes with three independent mol­ecules in the asymmetric unit, was prepared by the reaction of 3-meth­oxy-6-methyl­pyridine with KI and I2 in tetra­hydro­furan solution. In the crystal structure, the three independent mol­ecules are arranged in a similar orientation with the three polar meth­oxy groups aligned on one side and the three non-polar methyl groups on the other side. The three mol­ecules, excluding methyl H atoms, are essentially planar, with r.m.s. deviations of 0.0141 (1), 0.0081 (1) and 0.0066 (2)Å. The three pyridine rings make dihedral angles of 58.09 (3) 66.64 (4) and 71.5 (3)°. The crystal structure features rather weak inter­molecular C—H⋯O hydrogen bonds, which link two mol­ecules into dimers, and short I⋯N contacts [4.046 (3) Å]