One of the main challenges of the 21st century is to reduce CO2 emissions into the atmosphere for mitigating the impact of this greenhouse gas on global warming. Carbon Capture and Storage (CCS) technologies are costly and their implementation is still limited in practice. On the other hand, solar, wind and other renewable energies are not constantly available, thus requiring efficient methods for large-scale storage. A promising solution is the use of captured CO2 and H2 from renewable sources to produce liquid fuels and chemicals. This reaction has been scarcely applied in the industry because of the high costs associated with CO2 purification and transport, and the poor reactivity of CO2, which also forms undesirable side products. CO2pCat aims at tackling these limitations by developing porous nanomaterials enabling CO2 capture and its selective conversion into methanol and water.
CO2pCat wants to achieve this goal by combining computational simulations with experiments for a better understanding of CO2 transformation at the molecular level, and the design of improved systems by using machine learning algorithms. The transformation of computational models to real experimental systems is relatively easy with small metal-organic molecules, but challenging with nanomaterials. Recent developments in the synthesis of metal-organic-frameworks (MOFs) has changed this scenario allowing for the rational synthesis of these materials. On the other hand, the combination of machine learning algorithms with computational simulations accelerates the discovery of molecules enabling chemical transformations. The interdisciplinary expertise of the CO2pCat members, affiliated to both the Centre for Materials Science and Nanotechnology (SMN) and the Hylleraas Centre for Quantum Molecular Sciences, will facilitate the fulfilment of the CO2pcat goals.
One of the main challenges of the 21st century is the reduction of CO2 emissions into the atmosphere by replacing fossil fuels with renewable energy sources. The CO2pCat research project aims at developing a multivariate metal organic framework (MTV-MOF) involving functionalized linker cooperation for the selective hydrogenation of CO2 to methanol. Methanol is a versatile liquid and an ideal CO2 derivative when made by renewable hydrogen sources. However, efforts to perform this reaction with heterogeneous and homogeneous catalysts under mild conditions have failed at achieving high conversion with high selectivity. A combined approach, immobilizing well-defined molecular catalysts on porous materials has only yielded reduction of CO2 to formate. The use of MOFs with Cu(I) functionalized Zr-nodes leads to ethanol and requires a photosynthesizer to stabilize the catalyst. In CO2pCat, these limitations will be overcome by using linkers able to: 1) hold, and stabilize the active catalyst; 2) promote the cooperative activation of H2 via metal-ligand bifunctionality; and 3) promote the cooperative hydrogenation of formic acid via catalyst-amine cooperation. In order to achieve these goals, state-of-the-art computational methods and machine learning techniques will be used to design these linkers. The systems designed in silico will be implemented experimentally by using the recent advances in MOF synthesis by postsynthetic strategies. Computational methods combined with kinetic experiments will be used to get mechanistic insight into the cooperative activation of H2 and formic acid on reported and newly developed systems. In total, CO2pCat will provide chemical understanding on CO2 hydrogenation processes, a large database of catalyst-linkers able to hydrogenate CO2, and a single-site heterogeneous catalyst for the hydrogenation of CO2 to methanol.