Technology Readiness and Emerging Prospects of Coupled Catalytic Reactions for Sustainable Chemical Value Chains.

Transitioning from both the direct and indirect use of fossil fuels to the renewable and sustainable resources of the near future demands a focal shift in catalysis research - from investigating catalytic reactions in isolation to developing coupled reactions for modern chemical value chains. In this Perspective, we discuss the status and emerging prospects of coupled catalytic reactions across various scales and provide key examples. Besides being a sustainable and essential alternative to current fossil-based processes, the coupling of catalytic reactions offers novel and scalable pathways to value-added chemicals. By emphasizing the specific requirements and challenges arising from coupled reactions, we aim to identify and underscore research needs that are critical to expedite their development and to fully unlock their potential for chemical and fuel production.

Low-nuclearity CuZn ensembles on ZnZrOx catalyze methanol synthesis from CO2.

Metal promotion could unlock high performance in zinc-zirconium catalysts, ZnZrOx, for CO2 hydrogenation to methanol. Still, with most efforts devoted to costly palladium, the optimal metal choice and necessary atomic-level architecture remain unclear. Herein, we investigate the promotion of ZnZrOx catalysts with small amounts (0.5 mol%) of diverse hydrogenation metals (Re, Co, Au, Ni, Rh, Ag, Ir, Ru, Pt, Pd, and Cu) prepared via a standardized flame spray pyrolysis approach. Cu emerges as the most effective promoter, doubling methanol productivity. Operando X-ray absorption, infrared, and electron paramagnetic resonance spectroscopic analyses and density functional theory simulations reveal that Cu0 species form Zn-rich low-nuclearity CuZn clusters on the ZrO2 surface during reaction, which correlates with the generation of oxygen vacancies in their vicinity. Mechanistic studies demonstrate that this catalytic ensemble promotes the rapid hydrogenation of intermediate formate into methanol while effectively suppressing CO production, showcasing the potential of low-nuclearity metal ensembles in CO2-based methanol synthesis.

NMR-based quantification of liquid products in CO2 electroreduction on phosphate-derived nickel catalysts.

Recently discovered phosphate-derived Ni catalysts have opened a new pathway towards multicarbon products via CO2 electroreduction. However, understanding the influence of basic parameters such as electrode potential, pH, and buffer capacity is needed for optimized C3+ product formation. To this end, rigorous catalyst evaluation and sensitive analytical tools are required to identify potential new products and minimize increasing quantification errors linked to long-chain carbon compounds. Herein, we contribute to enhance testing accuracy by presenting sensitive 1H NMR spectroscopy protocols for liquid product assessment featuring optimized water suppression and reduced experiment time. When combined with an automated NMR data processing routine, samples containing up to 12 products can be quantified within 15 min with low quantification limits equivalent to Faradaic efficiencies of 0.1%. These developments disclosed performance trends in carbon product formation and the detection of four hitherto unreported compounds: acetate, ethylene glycol, hydroxyacetone, and i-propanol.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning.

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.