Development of an Automated Workflow for Screening the Assembly and Host-Guest Behaviour of Metal-Organic Cages towards Accelerated Discovery

Metal-organic cages (MOCs) have emerged as 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 and realization of MOCs with desirable properties. MOC discovery is thus often reliant on trial-and-error approaches and brute-force manual screening, resulting in long processes which are financially costly and material-intensive. Translating both the synthesis and property screening of MOCs to an automated high-throughput workflow is therefore attractive, not only to accelerate discovery, but to also provide the data sets 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 experimental screening of variables pertinent to MOC synthesis, high-throughput data curation and automated analysis, and development of a host:guest assay to rapidly assess binding behaviour. In addition, this automated experimental workflow was supplemented with computational modelling for post priori rationalization of the experimental outcomes. This study lays the groundwork for future large-scale MOC screening: even from a relatively modest screen of 24 precursor combinations under a single set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host:guest behaviour highlighted trends in binding and the identification of potential applications in molecular separations.