Conversion of carbon dioxide (CO 2 ) to fuels and chemicals with the help of renewable hydrogen (H 2 ) is a very attractive approach to reduce CO 2 emissions and replace dwindling fossil fuels. However, it is still a great challenge to synthesize aromatics directly from CO 2 hydrogenation, because CO 2 is thermodynamically very stable, and the aromatics are highly unsaturated products with complex structures. Here, we demonstrate that the combination of the sodium-modified spinel oxide ZnFeO x , which alone shows excellent performance for CO 2 hydrogenation to olefins, and hierarchical nanocrystalline HZSM-5 aggregates can realize a highly efficient synthesis of aromatics directly from CO 2 and H 2 . The maximum of aromatics selectivity was up to 75.6% among all hydrocarbons at 41.2% CO 2 conversion. Additionally, the selectivity toward CO and CH 4 is usually less than 20% over this catalyst system. The suitable amount of the residual sodium, hierarchical pore structure, and appropriate density of Brønsted acid sites endow the composite catalyst with an outstanding aromatics yield and high catalytic stability.

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Corrosion of metals in atmospheric environments is a worldwide problem in industry and daily life. Traditional anticorrosion methods including sacrificial anodes or protective coatings have performance limitations. Here, we report atomically thin, polycrystalline few-layer graphene (FLG) grown by chemical vapor deposition as a long-term protective coating film for copper (Cu). A six-year old, FLG-protected Cu is visually shiny and detailed material characterizations capture no sign of oxidation. The success of the durable anticorrosion film depends on the misalignment of grain boundaries between adjacent graphene layers. Theoretical calculations further found that corrosive molecules always encounter extremely high energy barrier when diffusing through the FLG layers. Therefore, the FLG is able to prevent the corrosive molecules from reaching the underlying Cu surface. This work highlights the interesting structures of polycrystalline FLG and sheds insight into the atomically thin coatings for various applications.

Two-dimensional (2D) materials and their in-plane and out-of-plane (i.e., van der Waals, vdW) heterostructures are promising building blocks for next-generation electronic and optoelectronic devices. Since the performance of the devices is strongly dependent on the crystalline quality of the materials and the interface characteristics of the heterostructures, a fast and nondestructive method for distinguishing and characterizing various 2D building blocks is desirable to promote the device integrations. In this work, based on the color space information on 2D materials’ optical microscopy images, an artificial neural network-based deep learning algorithm is developed and applied to identify eight kinds of 2D materials with accuracy well above 90% and a mean value of 96%. More importantly, this data-driven method enables two interesting functionalities: (1) resolving the interface distribution of chemical vapor deposition (CVD) grown in-plane and vdW heterostructures and (2) identifying defect concentrations of CVD-grown 2D semiconductors. The two functionalities can be utilized to quickly identify sample quality and optimize synthesis parameters in the future. Our work improves the characterization efficiency of atomically thin materials and is therefore valuable for their research and applications.