No abstract

The carboxylate ligand 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy)-based on the strongly fluorescent long-lifetime pyrene core-affords a permanently microporous fluorescent metal-organic framework, [In(2)(OH)(2)(TBAPy)].(guests) (1), displaying 54% total accessible volume and excellent thermal stability. Fluorescence studies reveal that both 1 and TBAPy display strong emission bands at 471 and 529 nm, respectively, upon excitation at 390 nm, with framework coordination of the TBAPy ligands significantly increasing the emission lifetime from 0.089 to 0.110 ms. Upon desolvation, the emission band for the framework is shifted to lower energy: however, upon re-exposure to DMF the as-made material is regenerated with reversible fluorescence behavior. Together with the lifetime, the emission intensity is strongly enhanced by spatial separation of the optically active ligand molecules within the MOF structure and is found to be dependent on the amount and chemical nature of the guest species in the pores. The quantum yield of the material is found to be 6.7% and, coupled with the fluorescence lifetime on the millisecond time scale, begins to approach the values observed for Eu(III)-cryptate-derived commercial sensors.

Porous materials find widespread application in storage, separation, and catalytic technologies. We report a crystalline porous solid with adaptable porosity, in which a simple dipeptide linker is arranged in a regular array by coordination to metal centers. Experiments reinforced by molecular dynamics simulations showed that low-energy torsions and displacements of the peptides enabled the available pore volume to evolve smoothly from zero as the guest loading increased. The observed cooperative feedback in sorption isotherms resembled the response of proteins undergoing conformational selection, suggesting an energy landscape similar to that required for protein folding. The flexible peptide linker was shown to play the pivotal role in changing the pore conformation

We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.