Closed-Loop Bayesian Optimization for the Synthesis of Amine-Grafted Resins for Direct Air Capture

Direct air capture (DAC) of CO 2 remains limited by adsorbent performance. Adsorption kinetics are a particularly underexplored lever to improve adsorbents, despite their significance in realistic processes. Today, the profile of 'the' optimal DAC adsorbent displaying high CO 2 uptake and selectivity as well as fast sorption kinetics remains unknown. Here, we present a closed-loop approach combining high-throughput synthesis and characterisation with Bayesian optimization to understand how the material synthesis space impacts adsorbent properties and how it can ultimately be optimized to enhance adsorption-based DAC performance. To carry out this study, we chose amine-functionalized porous adsorbents as our model system. By jointly optimizing adsorption kinetics and equilibrium CO 2 uptake at 400 ppm, the framework identifies high-performing materials while experimentally mapping a Pareto front. Sorbents with the highest CO 2 uptakes exhibit the slowest kinetics, and vice versa. We link this trade-off to the pore structure of the materials and how the pore geometry imposes diffusion constraints. The optimization study uncovers adsorbents with order-of-magnitude higher adsorption kinetics than benchmark adsorbents at half the uptakes. Beyond this model system, our Bayesian optimization framework is a blueprint for multi-objective materials optimization in fields beyond DAC adsorbents.