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Gradient Channel Segmentation in Covalent Organic Framework Membranes with Highly Oriented Nanochannels.

Xuechun JingMengxi ZhangZhenjie MuPengpeng ShaoYuhao ZhuJie LiBo WangXiao Feng
Published in: Journal of the American Chemical Society (2023)
Covalent organic frameworks (COFs) offer an exceptional platform for constructing membrane nanochannels with tunable pore sizes and tailored functionalities, making them promising candidates for separation, catalysis, and sensing applications. However, the synthesis of COF membranes with highly oriented nanochannels remains challenging, and there is a lack of systematic studies on the influence of postsynthetic modification reactions on functionality distribution along the nanochannels. Herein, we introduced a "prenucleation and slow growth" approach to synthesize a COF membrane featuring highly oriented mesoporous channels and a high Brunauer-Emmett-Teller surface area of 2230 m 2 g -1 . Functional moieties were anchored to the pore walls via "click" reactions and coordinated with Cu ions to serve as segmentation functions. This led to a remarkable H 2 /CO 2 separation performance that surpassed the Robeson upper bound. Moreover, we found that the functionalities distributed along the nanochannels could be influenced by functionality flexibility and postsynthetic reaction rate. This strategy paved the way for the accurate design and construction of COF-based artificial solid-state nanochannels with high orientation and precisely controlled channel environments.
Keyphrases
  • solid state
  • metal organic framework
  • deep learning
  • convolutional neural network
  • liquid chromatography
  • high throughput
  • quantum dots
  • machine learning
  • aqueous solution
  • highly efficient