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Generating value, in the form of functional chemicals, from biomass is a critical need of today. It is expected that replacing petroleum with biomass as the source of many commodity and fine chemicals can lead the transition away from a fossil-fuel based economy. To facilitate this transition, it is necessary to develop novel catalysts in conjunction with novel process methodologies based on sustainable, and green methods. New functional materials like metal-organic frameworks (MOFs) are expected to be at the forefront of this development on account of the various exceptional properties these materials possess like high internal surface area, and near infinite tunability in terms of its building blocks. Indeed, a number of catalysts for transformation of biomass derived platform chemicals based on MOFs have been reported in literature. In recent years, these materials have been further modified with polymers, bearing a number of different functionalities, providing the basis for the development of the next generation of functional materials. This thesis describes the design and development of a series of MOF/polymer supports for transition metal nanoparticles (MNPs). These MOF/polymer/MNP composites have been shown to have excellent utility for driving the selective catalytic upgrading of various biomass derived intermediates including, 5-hydroxymethylfurfural (HMF), and levulinic acid (LVA). We are able to show that there exist synergistic effects of the MOF and the polymer on the activity and the stability of the MNPs. We elucidate that by introducing polymers bearing large aromatic cores into MOFs, we are able to modify the mechanical stability of MOFs leading to more stable catalysts. In addition the MNPs show resistance to sintering because of the close proximity of the Lewis base sites. A combination of these effects lead to a significant improvement in the catalytic performance of the MOF/polymer/MNP composite catalysts which surpasses benchmark materials reported in literature.
Michael Graetzel, Hong Zhang, Dan Ren, Yelin Hu, Yunfei Jiao, Bing Wu, Chen Shen, Fei Liang