Abstract
Metal-organic cages (MOCs) are a class of self-assembled materials with promising applications in chemical purifications, sensing, and catalysis. Their potential is, however, hampered by challenges in the targeted design of MOCs with desirable properties. MOC discovery is thus often reliant on trial-and-error approaches and brute-force manual screening, which are time-consuming, costly, and material-intensive. Translating the synthesis and property screening of MOCs to an automated workflow is therefore attractive, to both accelerate discovery and provide the datasets crucial for data-led approaches to accelerate MOC discovery and to realize their targeted properties for specific applications. Here, an automated workflow for the streamlined assembly and property screening of MOCs was developed, incorporating automated high-throughput screening of variables pertinent to MOC synthesis, data curation and automated analysis, and development of a host-guest assay to rapidly assess binding behavior. Computational modelling supplemented this automated experimental workflow for post priori rationalization of experimental outcomes. This study lays the groundwork for future large-scale MOC screening: from a relatively modest screen of 24 precursor combinations under one set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host-guest behavior highlighted trends in binding and the identification of potential applications in molecular separations.