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Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework.


ABSTRACT: Hydrogen-bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals. Previously, we combined CSP with structure-property predictions to generate energy-structure-function (ESF) maps for a series of triptycene-based molecules with quinoxaline groups. From these ESF maps, triptycene trisquinoxalinedione (TH5) was predicted to form a previously unknown low-energy HOF (TH5-A) with a remarkably low density of 0.374 g cm-3 and three-dimensional (3D) pores. Here, we demonstrate the reliability of those ESF maps by discovering this TH5-A polymorph experimentally. This material has a high accessible surface area of 3,284 m2  g-1 , as measured by nitrogen adsorption, making it one of the most porous HOFs reported to date.

SUBMITTER: Shields CE 

PROVIDER: S-EPMC10952618 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework.

Shields Caitlin E CE   Wang Xue X   Fellowes Thomas T   Clowes Rob R   Chen Linjiang L   Day Graeme M GM   Slater Anna G AG   Ward John W JW   Little Marc A MA   Cooper Andrew I AI  

Angewandte Chemie (International ed. in English) 20230503 34


Hydrogen-bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals. Previously, we combined CSP with structure-property predictions to generate energ  ...[more]

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