Direct air capture (DAC) of CO2 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 CO2 uptake and selectivity as well as fast sorption kinetics remains unknown.
In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.
Covalent organic frameworks (COFs) are versatile materials platforms for precise function integration owing to their high crystallinity, large surface areas, tunable characteristics and diverse and predictable structures. However, the dominant solvothermal method for COF synthesis requires harsh conditions, including high temperatures, toxic organic solvents, sealed and pressurized reactors, and extended reaction times that often exceed several days.
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