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◆ ACS Omega2026-03-23· Catalysis

Emerging Trends and Future Prospects of the Catalytic Conversion of Syngas to Higher Alcohols

Biswajit Shown, Pranjal Gogoi, Shouvik Mitra, Indrajit Shown, Rajib Bandyopadhyay, Asit Kumar Das

原始摘要(英文原文)· Original abstract
The catalytic conversion of syngas, a mixture of carbon monoxide (CO) and hydrogen (H2), into higher alcohols (C2+OH) in a single step holds great promise for sustainable fuels and chemical production. These higher alcohols (HAs) serve as value-added and versatile intermediates for a wide range of industries. This is one of the promising pathways to produce green energy and chemicals if syngas is generated from lignocellulosic biomass. Despite four decades of research in the direct conversion of syngas to HAs, many challenges remain in the stability of the catalyst, achieving high selectivity for the desired C2+ alcohol yield, and CO conversion, owing to an intricate reaction network and contending pathways. Recent advances in catalyst design, particularly multifunctional catalyst systems, active metal–support interactions, and nonconventional promoters, have significantly improved catalytic performance. In addition, novel support materials and nanostructured catalysts have unlocked new avenues for tuning catalyst activity, HA selectivity, and stability. Beyond materials, developments in reactor design, process intensification, and computational modeling also provide deeper mechanistic insights, enabling the development of desired catalyst systems to produce HAs in a single pot. This review highlights recent progress across all four primary catalytic systems─Rh-based, Mo-based, modified FT (Fischer–Tropsch), and modified MS (methanol synthesis). Emerging trends in catalyst design, the use of multifunctional catalyst systems, tandem and single-atom catalysts, catalysts based on MOF (metal–organic framework), zeolites, and nonconventional carbonaceous materials are discussed. Additionally, the understanding of reaction mechanisms and the role of AI–ML (artificial intelligence–machine learning) in fast-track catalyst screening and formulation have also been briefly discussed. Finally, our insights and perspectives on the topic to inspire further exploration in this emerging field are provided.
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