Metal-organic frameworks—sponge-like crystalline materials with pores that can trap gases, deliver drugs, or catalyse chemical reactions—are currently discovered and optimised through slow, wasteful trial-and-error methods that are difficult to reproduce. This project builds automated flow microwave reactors that can run reactions continuously, analyse the results in real time, and use evolutionary algorithms to self-optimise the synthesis of these materials without human intervention. The core problem is that no one fully understands how MOFs form, so researchers cannot design them for specific applications or scale up production reliably. By combining automated experimentation with fundamental studies of crystallisation, this work aims to replace guesswork with predictable, reproducible synthesis. If successful, the approach could cut the time and energy needed to produce MOFs for carbon capture, drug delivery, and chemical manufacturing from years to weeks. It would also make commercial scale-up feasible, unlocking a market projected to exceed £20 billion by 2032. The underlying automation and self-optimisation methods could transfer to other advanced materials, quietly improving industrial processes that depend on precisely structured porous solids.
View original technical description
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.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know