DFT-Modellierungen als Werkzeug für die rationale Entwicklung molekularer Katalysatoren : Transformationen von CO$ _2 $ - katalysiert durch späte Übergangsmetallkomplexe (2016)

In this Ph.D. project it was examined how a combined density functional theory (DFT) and experimental approach may enhance research on homogenously catalyzed reactions on the example of carbon dioxide transformations with late transition metal complexes as catalyst. Therefore three reaction systems I – III have been studied by computational and partly by experimental means.In system I a CO2 hydrogenation with sodium hydroxide as a base was examined with 1 or 2 as catalyst respectively. This system is quite well known as Yang did a computational study on a complex very similar to 1,[1] while Milstein investigated 1 as a catalyst in the CO2 hydrogenation under slightly different conditions though.[2] In the Ph.D. project those results where expanded by also using 2 as a catalyst for the CO2 hydrogenation. It was known previously that 2 may be transformed under reaction conditions to a ruthenium analogue of 1.The study of the complex reaction network of this system should give insights into mechanistic details of the iron and ruthenium catalyst systems. In order to evaluate the catalyst by the DFT calculations, the energy span model was applied to extract a DFT based catalyst activity (turn over frequency, TOF) for the iron as well as the ruthenium complex from the computations. Furthermore kinetic experiments have been conducted to determine an experimental catalyst activity. For better comparison the TOF was transformed to an effective activation barrier ΔG‡DFT and ΔG‡EXP. This showed, that the DFT based values match the experimental ones within experimental accuracy, which means that computations may yield accurate results even for challenging reaction systems. DFT as well as experiment also showed, that the catalytic activity of the iron system is very close to that of the ruthenium system.In system II the CO2 hydrogenation with triethylamine as base and 3 or 4 as catalyst was examined respectively. Both compounds were not known to catalyze this transformation previously. An initial DFT calculation predicted 3 to be an active catalyst but also being highly prone to deactivation by protonation of the phenylpyridinyl ligand. This could then be shown in NMR experiments. For compound 4 the DFT calculations predicted an effective activation barrier of 20.7 kcal/mol and no deactivation. In laboratory experiments 4 proved to be stable over 48 hours at reaction conditions and exhibited an effective activation barrier of 23.0 kcal/mol, again supporting DFT predictions.In system III it was investigated if the previously studied carboxylation of benzene may be adapted to a carboxylation of methane.[3] Therefore an analogue reaction mechanism was the base for a catalyst screening with 5 as lead structure. By varying key components like the ligand backbone, the halogen or the metal center in different solvent environments, several catalyst candidates could be generated and the energy span for each one was calculated by DFT simulations. Two candidates with energy spans of 30.5 kcal/mol and 34.5 kcal/mol in toluene could be found.An energy span of 30 kcal/mol equals a predicted TOF of about 0.1 per hour at 100 °C. This means that the first catalyst candidate might show an observable activity for the carboxylation of methane under presence of potassium-tert-butoxide as base at 100 °C. The structural motive of the second complex is known to be stable at reaction conditions of 200 °C, which would correspond to an activity of about 40 per hour based on an energy span of 34.5 kcal/mol. Those predictions are made with the assumption that the catalyst candidates are stable under the investigated conditions and no side reactions take place.Overall it could be shown that DFT calculations are a versatile tool in homogenous catalyst research especially when combined with selected experimental techniques. Smart combination of computations with experiments yield complementary results which may enhance the quality of the generated knowledge significantly.Literature[1]X. Yang, ACS Catal. 2011, 1, 849-854.[2]R. Langer et al., Angew. Chem. Int. Ed. 2011, 50, 9948-9952.[3]A. Uhe et al., Chem. - Eur. J. 2012, 18, 170-177.