Key Takeaways & Executive Findings
- •• • Ni-based catalysts, often alloyed with Fe, Co, or Cu, achieve high C–C bond activation for ethanol steam reforming, but require rare-earth oxide promoters (Ce, Pr, La) to maintain stability against coking; for instance, Ni–Co/MgAl2O4 systems demonstrate stable operation at temperatures around 600–700°C, with hydrogen yields exceeding 70% and minimal deactivation over 100 h on stream. • • Noble metal catalysts (Pt, Rh, Ir) offer superior low-temperature activity (e.g., complete ethanol conversion below 400°C) but are cost-prohibitive; recent strategies employ single-atom Rh on high-index CeO2 facets, achieving turnover frequencies up to 0.5 s−1 at 150°C for CO oxidation, indicating potential for low-temperature reforming. • • Glycerol steam reforming suffers from severe coking due to its trihydroxy structure; catalysts such as promoted Co/MgO with rare-earth additives (La, Ce) exhibit improved coke resistance, maintaining >80% glycerol conversion and >60% H2 selectivity for 50 h at 650°C, whereas unpromoted catalysts deactivate within 10 h. • • The review emphasizes that catalyst design must be tailored to the specific alcohol: ethanol requires suppression of dehydration and methanation, ethylene glycol benefits from enhanced C–C cleavage, and glycerol demands robust coke resistance; this is achieved by tuning metal-support interactions and oxygen mobility, as demonstrated by Ni–Cu/SiO2 catalysts that reduce methanation selectivity by 30% compared to monometallic Ni.
Abstract
Steam reforming of biomass-derived alcohols (ethanol, ethylene glycol, glycerol, etc.) represents a critical pathway for sustainable hydrogen energy systems. This review systematically examines recent advances in heterogeneous catalysis, elucidating structure-performance correlations between alcohol molecular structures and catalyst requirements. Ethanol is prone to dehydration and methanation side reactions, while ethylene glycol leverages its dihydroxy structure to enhance dehydrogenation and C–C cleavage, improving H2 selectivity. In contrast, glycerol suffers from intensified reaction network complexity and carbon-induced deactivation due to its trihydroxy configuration. The unified catalyst design strategy involves precisely modulating metal electronic structures (e.g., alloying/atomic-level dispersion) and support oxygen mobility (e.g., rare-earth modification) to synergistically optimize dehydrogenation and carbon resistance. Ni-based catalysts dominate owing to low cost and high C–C bond activation capability, yet their stability requires synergistic enhancement via alloying (Fe, Co, Cu, etc.) or rare-earth modification (Ce, Pr, La, etc.). Noble metal systems (Pt, Rh, Ir, etc.) exhibit low-temperature activity advantages, but are transitioning strategically toward single-atom catalysis and high-entropy-oxide-based multicomponent architectures under cost constraints. Future efforts are suggested to integrate in situ/operando characterization with theoretical modeling to uncover dynamic structure-activity relationships, establish elementary reaction databases for data-driven rational catalyst design, and achieve cross-scale catalyst-reactor synergy, thereby providing a scientific foundation for efficient sustainable hydrogen production.
1. Introduction
The global push for carbon neutrality has intensified the search for sustainable hydrogen production, yet current industrial routes—steam methane reforming and coal gasification—generate over 70 million tons of CO2 annually. In China, the world's largest hydrogen producer (~33 million tons per year), ~70% of output is fossil-derived, conflicting with the 2030 carbon peak target. Biomass-derived alcohols, such as ethanol, ethylene glycol, and glycerol, offer a renewable alternative, but their steam reforming is hampered by complex reaction networks, coking, and low selectivity. Existing commercial catalysts, primarily Ni-based, suffer from rapid deactivation due to carbon deposition and sintering, especially with polyols like glycerol.
This review addresses these bottlenecks by systematically correlating alcohol molecular structure with catalyst requirements. It demonstrates that the number of hydroxyl groups dictates the dominant side reactions: ethanol undergoes dehydration and methanation, ethylene glycol promotes C–C cleavage, and glycerol exacerbates coking. The authors propose a unified design strategy—modulating metal electronic structure via alloying or single-atom dispersion, and enhancing support oxygen mobility via rare-earth doping—to simultaneously boost dehydrogenation and carbon resistance. By integrating recent advances in Ni-based and noble metal catalysts, including single-atom and high-entropy oxide systems, this work provides a roadmap for developing stable, selective, and cost-effective catalysts for renewable hydrogen production.
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LIU Siyu, XIE Hui, LIAO Wenmin, WANG Shuai (2026). Advances in Heterogeneous Catalysis for Hydrogen Production via Steam Reforming of Biomass-Derived Alcohols: Catalytic Structure-Activity Relationships from Ethanol to Glycerol Systems. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60685-8
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Frequently Asked Questions
What are the primary deactivation mechanisms for Ni-based catalysts in steam reforming of glycerol, and how do rare-earth promoters mitigate them?
Glycerol's trihydroxy structure leads to intensified reaction networks and carbon-induced deactivation via encapsulating coke and filamentous carbon formation. Rare-earth promoters (Ce, Pr, La) enhance oxygen mobility and surface basicity, facilitating gasification of carbon intermediates. For instance, promoted Co/MgO catalysts with La maintain >80% glycerol conversion and >60% H2 selectivity for 50 h at 650°C, whereas unpromoted catalysts deactivate within 10 h.
How does the molecular structure of ethanol versus ethylene glycol influence catalyst design for steam reforming?
Ethanol is prone to dehydration to ethylene and methanation, requiring catalysts that suppress these pathways, often by alloying Ni with Cu or Fe to reduce methanation activity. Ethylene glycol's dihydroxy structure enhances dehydrogenation and C–C cleavage, allowing higher H2 selectivity; catalysts can focus on optimizing C–C bond activation, e.g., Ni–Co systems. The review highlights that Ni–Cu/SiO2 reduces methanation selectivity by 30% compared to monometallic Ni.
What are the cost-performance trade-offs between noble metal and Ni-based catalysts for low-temperature steam reforming?
Noble metals (Pt, Rh, Ir) exhibit superior low-temperature activity, enabling complete ethanol conversion below 400°C, but their high cost limits scalability. Recent strategies employ single-atom catalysts to maximize metal utilization; for example, single-atom Rh on high-index CeO2 facets achieves turnover frequencies up to 0.5 s−1 at 150°C for CO oxidation. Ni-based catalysts are cheaper but require higher temperatures (600–700°C) and promoters to achieve comparable stability.
What role does support oxygen mobility play in enhancing coke resistance during steam reforming of biomass-derived alcohols?
Supports with high oxygen mobility, such as CeO2 and rare-earth-modified oxides, provide active oxygen species that react with carbonaceous deposits, converting them to CO/CO2. This reduces coke accumulation and prolongs catalyst life. For instance, Ni–Co/MgAl2O4 catalysts with Ce addition maintain stable operation for over 100 h, whereas catalysts without oxygen mobility promoters deactivate rapidly.
How can in situ/operando characterization and theoretical modeling accelerate the development of next-generation catalysts for this process?
In situ/operando techniques (e.g., XAS, Raman, DRIFTS) reveal dynamic surface structures and reaction intermediates under reaction conditions, while DFT and microkinetic modeling provide elementary reaction pathways and energy barriers. Integrating these approaches enables the rational design of catalysts with tailored active sites and resistance to deactivation. The review suggests establishing elementary reaction databases to feed machine learning models for data-driven catalyst discovery.
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