A General Synthesis Method for Covalent Organic Framework and Inorganic 2D Materials Hybrids

Two-dimensional(2D)inorganic/organic hybrids provide a versatile platform for diverse applications,including electronic,catalysis,and energy storage devices.The recent surge in 2D covalent organic frameworks(COFs)has introduced an organic counterpart for the development of advanced 2D organic/inorganic hybrids with improved electronic coupling,charge separation,and carrier mobility.However,existing synthesis methods have primarily focused on few-layered film structures,which limits scalability for practical applications.Herein,we present a general synthesis approach for a range of COF/inorganic 2D material hybrids,utilizing 2D inorganic materials as both catalysts and inorganic building blocks.By leveraging the intrinsic Lewis acid sites on the inorganic 2D materials such as hexagonal boron nitride(hBN)and transition metal dichalcogenides,COFs with diverse functional groups and topologies can grow on the surface of inorganic 2D materials.The controlled 2D morphology and excellent solution dispersibility of the resulting hybrids allow for easy processing into films through vacuum filtration.As proof of concept,hBN/COF films were employed as filters for Rhodamine 6G removal under flow-through conditions,achieving a removal rate exceeding 93%.The present work provides a simple and versatile synthesis method for the scalable fabrication of COF/inorganic 2D hybrids,offering exciting opportunities for practical applications such as water treatment and energy storage.

Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy

Understanding lattice deformations is crucial in determining the properties of nanomaterials,which can become more prominent in future applications ranging from energy harvesting to electronic devices.However,it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area.Here,we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials.Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy(4D-STEM).Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample.Using this purely data-driven analysis,we have uncovered different types of material deformations,such as strain,lattice distortion,bending contour,etc.,which can significantly impact the band structure and subsequent performance of nanomaterials-based devices.We envision that this data-driven procedure will provide insight into materials’intrinsic structures and accelerate the discovery of materials.