Engineering porous materials with precisely targeted properties using AI-driven self-optimising continuous flow microwave technologies

Metal-organic frameworks (MOFs) are porous materials comprised of metal nodes/clusters and organic linkers. MOFs have attracted extensive interest from academia and industry owing to their unprecedented porosity and structural and functional diversity; the MOF market is set to reach >£20bn by 2032 (Global Market Insights Inc.). Applications of MOFs including sensors, catalysts (e.g. to transform carbon dioxide into chemical feedstocks), drug delivery and in pollutant capture offer huge potential for addressing key global challenges in healthcare, energy, and mitigation of environmental pollution. However, there is limited understanding of the formation processes of MOFs and current methods for discovering and optimising MOFs rely on trial-and-error and are poorly reproducible. Consequently, a targeted materials discovery and optimization is not possible, the complexity of materials produced is limited, and scale-up takes many years/is not possible as conditions optimised in batch are not readily translatable to scaled up processing.

The proposed research will revolutionise the way in which MOFs are discovered, prepared, and applied by redressing gaps in mechanistic understanding of reactions and providing new synthetic protocols for targeted synthesis, including routes to scale-up. This will be achieved by developing automated flow microwave platforms equipped with real-time analyses capable of self-optimization guided by evolutionary algorithms; underpinned by new fundamental understanding of crystallisation processes for MOFs. This will enable faster production of MOFs for targeted applications (e.g. catalysis, drug delivery) without wasting time, energy, or chemical resources and overcome considerable issues with reproducibility, which currently hinders MOF research and their commercial exploitation.