Methanol Reforming for Hydrogen Production: Advances in Catalysts, Nanomaterials, Reactor Design, and Fuel Cell Integration

IF 5.1 Q2 ENGINEERING, CHEMICAL
Muhammad Usman,  and , Tetsuya Yamada*, 
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Abstract

Methanol reforming has emerged as a leading pathway for on-demand hydrogen production, particularly for applications in portable power and fuel cells. This review offers a comprehensive analysis of methanol steam reforming (MSR), partial oxidation (POX), autothermal reforming (ATR), and recent integration strategies with renewable systems and fuel cells. Emphasis is placed on catalyst design, reaction mechanisms, reactors, operational parameters, and recent nanostructured catalyst innovations, such as single-atom catalysts (SACs), bimetallic, carbon nanotubes, perovskites, and alloy nanomaterials. This review critically evaluates recent progress, highlighting how tailored catalyst morphologies, metal–support interactions, and synthesis methods translate into enhanced methanol conversion, H2 selectivity, and CO suppression. Notably, low-temperature SACs and Zn-modified bimetallic systems exhibit remarkable performance metrics, pointing toward viable pathways for clean hydrogen production. Furthermore, emerging approaches like plasma-assisted dry reforming and chemical looping integration present promising solutions for CO2 utilization. Recent applications of artificial intelligence (AI) in catalyst screening and reaction modeling also show potential to accelerate the discovery of high-efficiency systems. By synthesizing these findings and identifying the gaps in current research, this review outlines future directions for scalable, low-emission methanol reforming technologies, aiming to support the global transition toward a hydrogen-based energy economy.

甲醇重整制氢:催化剂、纳米材料、反应器设计和燃料电池集成的进展
甲醇重整已成为按需制氢的主要途径,特别是在便携式电源和燃料电池中的应用。本文综述了甲醇蒸汽重整(MSR)、部分氧化(POX)、自热重整(ATR)以及最近与可再生系统和燃料电池的整合策略。重点放在催化剂设计,反应机制,反应器,操作参数,以及最近的纳米结构催化剂创新,如单原子催化剂(SACs),双金属,碳纳米管,钙钛矿和合金纳米材料。这篇综述批判性地评估了最近的进展,重点介绍了量身定制的催化剂形态、金属支撑相互作用和合成方法如何转化为增强的甲醇转化率、H2选择性和CO抑制。值得注意的是,低温sac和锌改性双金属系统表现出卓越的性能指标,为清洁制氢指明了可行的途径。此外,等离子体辅助干重整和化学环整合等新兴方法为二氧化碳利用提供了有希望的解决方案。最近人工智能(AI)在催化剂筛选和反应建模方面的应用也显示出加速发现高效系统的潜力。通过综合这些发现并确定当前研究中的差距,本综述概述了可扩展的低排放甲醇重整技术的未来方向,旨在支持全球向氢基能源经济的过渡。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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