ABSTRACT
More than 85 per cent of all chemical industry products are made using catalysts, the overwhelming majority of which are heterogeneous catalysts that function at the gas-solid interface. Consequently, much effort is invested in optimizing the design of catalytic reactors, usually by modelling the coupling between heat transfer, fluid dynamics and surface reaction kinetics. The complexity involved requires a calibration of model approximations against experimental observations, with temperature maps being particularly valuable because temperature control is often essential for optimal operation and because temperature gradients contain information about the energetics of a reaction. However, it is challenging to probe the behaviour of a gas inside a reactor without disturbing its flow, particularly when trying also to map the physical parameters and gradients that dictate heat and mass flow and catalytic efficiency. Although optical techniques and sensors have been used for that purpose, the former perform poorly in opaque media and the latter perturb the flow. NMR thermometry can measure temperature non-invasively, but traditional approaches applied to gases produce signals that depend only weakly on temperature are rapidly attenuated by diffusion or require contrast agents that may interfere with reactions. Here we present a new NMR thermometry technique that circumvents these problems by exploiting the inverse relationship between NMR linewidths and temperature caused by motional averaging in a weak magnetic field gradient. We demonstrate the concept by non-invasively mapping gas temperatures during the hydrogenation of propylene in reactors packed with metal nanoparticles and metal-organic framework catalysts, with measurement errors of less than four per cent of the absolute temperature. These results establish our technique as a non-invasive tool for locating hot and cold spots in catalyst-packed gas-solid reactors, with unprecedented capabilities for testing the approximations used in reactor modelling.
