CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning.

Abstract

The emission of CO2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon-neutrality by 2050, CO2 capture and utilization is crucial. The efficient conversion of CO2 to C5+ gasoline and aromatics remains elusive mainly due to CO2 thermodynamic stability and the high energy barrier of the C-C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.