Nency P. Domingues

Abstract Pyrene-based metal-organic frameworks (MOFs) have tremendous potential for various applications. With infinite structural possibilities, the MOF community often relies on simulations to identify the most promising candidates for given applications. Among thousands of reported structures, many exhibit limited reproducibility — in either synthesis, performance, or both — owing to the sensitivity of synthetic conditions.

Pyrene-based metal–organic frameworks (MOFs) have been increasingly used in the fields of luminescence sensing, (1−3) photocatalysis, (4−7) electrochemistry, (8,9) as well as biomedical applications. (10) Moreover, in recent years, the interest in pyrene-based MOFs for gas adsorption and separation has widely grown. (11) Various characteristics make pyrene-MOFs very attractive CO2-adsorbents: they possess high water stability and retain permanent porosity.

Abstract The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications.