Smit, BerendStylianou, KyriakosChidambaram, Arunraj2020-05-122020-05-122020-05-12202010.5075/epfl-thesis-7971https://infoscience.epfl.ch/handle/20.500.14299/168698Metal-Organic Frameworks (MOFs) are an emerging class of materials that consist of metal ions which are linked with organic ligands via coordination bonds to form extended networks. MOF structures consists predominantly of open voids which makes them amongst the most porous materials synthesized to date. The aim of this thesis is to contribute to the development of a platform for a systematic study of the influence of synthetic conditions on MOFs and thereby, assess how this can affect the surface area of MOFs. To achieve this aim, a platform that utilises high-throughput robotic synthesis guided by genetic algorithm and machine learning was devised. This led to a rational synthesis of a MOF with the highest surface area reported to date. MOFs are considered as promising adsorbents for carbon capture from flue gasses emitted from fossil fuel fired power plants. Whilst there are many MOFs that are optimised for separation of CO2 from N2, their separation capability is detrimentally affected when they are subjected to realistic flue gas conditions that contains H2O vapour. This is due to the competition between CO2 and H2O for the same adsorption sites. To address this, through close collaboration with computational scientists, we discovered a new class of materials for CO2/H2O separations.enmetal-organic frameworksMOFsporoussurface areahigh-throughput robotic synthesisgenetic algorithmscarbon captureflue gasCO2H2Othesis::doctoral thesis