Catalytic Upgrading of a Mixed Hydroxy Acid Feedstock Derived from Kraft Black Liquor.

Lignocellulosic feedstocks are widely studied for sustainable liquid fuel and chemical production. The pulp and paper industry generates large amounts of kraft black liquor (BL) from which a high volume of hydroxy acids (HAs) can be separated for further catalytic processing. Here, we explore the catalytic upgrading of HAs, including the conversion of (1) a model HA, gluconic acid; (2) a model mixture of HAs, and (3) a real mixture of HAs derived from kraft BL on M/Nb2O5 (M = Pd, Pt, Rh, and Ru). The hydrodeoxygenation of model gluconic acid reveals that "volatile" carboxylic acids (mainly C2 and C3), levulinic acid, and cyclic esters are significant products over all the catalysts, with Pd/Nb2O5 showing superior activity and selectivity toward valuable intermediates. The model mixture of HAs shows a wide range of reactivity over the supported metal catalyst, with the product selectivity strongly correlating to reaction temperature. Utilizing a 0.25% Pd/Nb2O5 catalyst, a real mixture of HAs derived from kraft BL is successfully dehydroxylated to produce a mixture rich in C3-C8 carboxylic acids that may be amenable for further upgrading, e.g., catalytically to ketones with high carbon chain lengths. Despite the feedstock complexity, we selectively cleaved the C-OH bonds of HAs, while successfully preserving most of the -COOH groups and minimizing C-C and C=O bond scission reactions under the operating conditions tested. The BL-derived HA stream is thus proposed to be a suitable platform for producing mixed carboxylic acid products from an overoxygenated byproduct feed.

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

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.