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Aggregated Structures of Two-Dimensional Covalent Organic Frameworks.

Chengjun KangZhaoqiang ZhangAdam K UsadiDavid C CalabroLisa Saunders BaughKexin YuYuxiang WangDan Zhao
Published in: Journal of the American Chemical Society (2022)
Covalent organic frameworks (COFs) have found wide applications due to their crystalline structures. However, it is still challenging to quantify crystalline phases in a COF sample. This is because COFs, especially 2D ones, are usually obtained as mixtures of polycrystalline powders. Therefore, the understanding of the aggregated structures of 2D COFs is of significant importance for their efficient utilization. Here we report the study of the aggregated structures of 2D COFs using 13 C solid-state nuclear magnetic resonance ( 13 C SSNMR). We find that 13 C SSNMR can distinguish different aggregated structures in a 2D COF because COF layer stacking creates confined spaces that enable intimate interactions between atoms/groups from adjacent layers. Subsequently, the chemical environments of these atoms/groups are changed compared with those of the nonstacking structures. Such a change in the chemical environment is significant enough to be captured by 13 C SSNMR. After analyzing four 2D COFs, we find it particularly useful for 13 C SSNMR to quantitatively distinguish the AA stacking structure from other aggregated structures. Additionally, 13 C SSNMR data suggest the existence of offset stacking structures in 2D COFs. These offset stacking structures are not long-range-ordered and are eluded from X-ray-based detections, and thus they have not been reported before. In addition to the dried state, the aggregated structures of solvated 2D COFs are also studied by 13 C SSNMR, showing that 2D COFs have different aggregated structures in dried versus solvated states. These results represent the first quantitative study on the aggregated structures of 2D COFs, deepen our understanding of the structures of 2D COFs, and further their applications.
Keyphrases
  • high resolution
  • magnetic resonance
  • magnetic resonance imaging
  • computed tomography
  • solid state
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  • big data
  • artificial intelligence