Photocatalytic Reaction in Monolithic Optical Fiber Reactor with Inverse Opal Catalyst

The development of photocatalytic reactor is essential for the successful application of heterogeneous semiconductor in environmental study, which has been shown to be photoactive and effective to oxidate organic pollutant and photoreduce CO2 to useful compounds. In this dissertation, a monolithic optical fiber reactor (MOFR) coated with inverse opal titania, which uses optical fibers as light-transmitting conductor and support of catalyst, was developed for both photodegradation and photoreduction. 1,2-dichlorobenzene, a volatile organic compound (VOC), was selected as the organic pollutant. This configuration of reactor and catalyst provides a high surface area, enhances mass transfer within the catalyst, manipulates photons transmission within fibers, and provides higher quantum efficiency. The effects of flow rate, UV intensity, humidity (water vapor pressure) and temperature were investigated for the photodegradation in gaseous phase. The results show that flow rate and UV intensity determine the reaction regime simultaneously. Higher humidity can significantly decrease the photoreaction. Inverse opal titania shows higher quantum efficiency than conventional P25 catalyst in this study. This configuration can also work in an aqueous phase to degrade organic compounds. With inverse opal titania doped with Cu, MOFR can be used to photoreduce CO2 to methanol at mild experimental conditions. The effects of water vapor pressure, flow rate and UV intensity were investigated in detail and optimized. The results show there is an optimal value for the water vapor pressure in this study. In addition, inverse opal catalyst shows higher quantum efficiency for reduction. A three-dimensional model was developed to simulate the process of photodegradation both in gaseous phase and aqueous phase. A convection diffusion model, reaction kinetics model and UV radiance model in optical fiber were incorporated. Reasonable agreement between experimental results and model-predicted results was found. This model certainly explains the experimental results. So it is used to select optimal value for each experiment parameters in MOFR