Understanding Liquid Phase Heterogeneous Catalysis to Develop Catalytic Processes

Abstract

Herein, a combination of total neutron scattering (TNS), nuclear magnetic resonance (NMR) and computational simulation has been used to probe bulk and confined mixtures of benzene and cyclohexane. The hydrogenation of benzene to cyclohexane has been used in these studies as a model study with some industrial relevance. The iterative development of a newly combined total TNS and NMR technique, entitled NeuNMR, has been a focal point of this study. This in-situ method enables time-resolved chemical composition to be obtained, an improvement on traditional TNS experiments. As a result coordinating kinetic data on the pseudo-static hydrogenation of benzene, using an MCM-41 supported catalyst, has been acquired by analysis of the evolution of the neutron scattering data total structure factors and the evolution of the coordinated NMR intensities. A secondary continuous flow NeuNMR method has been established to facilitate longer data collection times, this flow system has been used to deposit cyclohexane-benzene mixtures intended to simulate points along the reaction pathway. Empirical potential structure refinement (EPSR) has been used to iteratively derive computational models, using known chemical and physical constraints, towards the experimentally obtained total neutron scattering data. The fits displayed by the constructed simulations, particularly in the Q regions reflecting the most critical intermolecular and atomistic interactions, are in excellent agreement with the experimental total structure factors. The use of this simulation method allows the molecular ordering and orientations of the species under confinement to be probed, and contrasted with that of the bulk mixtures – studied by conventional total neutron scattering procedures. In the confined systems where high benzene concentrations are present, an increased preference for parallel orientations has been displayed for like benzene molecules. Further analysis of the optimised simulations are included.

Date of Award	1 Aug 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Arthur Garforth (Supervisor) & Chris Hardacre (Supervisor)