用于动态增强甲烷氧化的定制铂族金属/尖晶石氧化物催化剂

IF 4.3 Q2 ENGINEERING, CHEMICAL
Pak Wing Chen, Debtanu Maiti, Ru-Fen Liu, Lars C. Grabow* and Michael P. Harold*, 
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引用次数: 0

摘要

一项实验和分子建模相结合的研究发现了一系列尖晶石氧化物,它们与 PGM(铂族金属)结合可增强甲烷氧化活性。随着减少温室气体(GHG)排放的迫切需要,人们对在交通、商业和工业应用中使用天然气汽车(NGV)和天然气发动机(NGE)重新产生了兴趣。NGV 和 NGE 排放的二氧化碳少于石油来源的同类产品,但可能会排放未燃烧的甲烷,而甲烷是一种更强的温室气体。对于化学计量发动机,甲烷氧化催化剂在层状结构中含有 PGM 和尖晶石氧化物,可提高甲烷氧化活性并降低熄火温度 (T50)。可还原尖晶石氧化物具有直接和间接的作用,这些作用可有效地通过体氧空位形成能(Evac)来描述。我们应用密度泛函理论(DFT)确定了几种具有良好 Evac 的富土、富钴尖晶石氧化物,这些 Evac 与动态氧储存能力(DOSC)以及 CO 和 H2 氧化活性相关。我们通过实验对 DFT 确定的尖晶石氧化物与 Pt+Pd 结合使用的甲烷氧化活性进行了排序,在时间不变和调制进料条件下进行了测量。我们发现尖晶石氧化物的活性与 DFT 计算的还原性之间存在良好的一致性。研究结果表明,尖晶石还原性是提高低温甲烷转化率的一个关键因素,它通过甲烷在 PGM 位点上的活化和随后在尖晶石氧化物上的中间产物和副产物的氧化之间的平衡来实现。与计算预测的 Evac 相一致,NiCo2O4 被证实在测试的尖晶石样品中具有最高的 DOSC 和最低的 T50。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Tailored Platinum Group Metal/Spinel Oxide Catalysts for Dynamically Enhanced Methane Oxidation

Tailored Platinum Group Metal/Spinel Oxide Catalysts for Dynamically Enhanced Methane Oxidation

Tailored Platinum Group Metal/Spinel Oxide Catalysts for Dynamically Enhanced Methane Oxidation

A combined experimental and molecular modeling study identifies a family of spinel oxides that in combination with PGM (platinum group metals) provide enhanced methane oxidation activity. With a reduction in greenhouse gas (GHG) emissions urgently needed, there is renewed interest in the use of natural gas vehicles (NGVs) and engines (NGEs) for transportation, commerce, and industrial applications. NGVs and NGEs emit less CO2 than their petroleum-derived counterparts but may emit uncombusted methane, an even more potent GHG. For stoichiometric engines, methane oxidation catalysts containing PGM and spinel oxide in layered architectures offer increased methane oxidation activity and lower light-off temperatures (T50). The reducible spinel oxide has direct and indirect roles that are effectively described by the bulk oxygen vacancy formation energy (Evac). We apply density functional theory (DFT) to identify several earth-abundant, cobalt-rich spinel oxides with favorable Evac, shown to correlate with dynamic oxygen storage capacity (DOSC) and CO and H2 oxidation activity. We experimentally rank-order the DFT-identified spinel oxides in combination with Pt+Pd for their methane oxidation activity measurements, under both time-invariant and modulated feed conditions. We show good agreement between the activity and the DFT-computed reducibility of the spinel oxide. The findings suggest spinel reducibility is a key factor in achieving enhanced low-temperature methane conversion, enabled through a balance of methane activation on the PGM sites and subsequent oxidation of the intermediates and byproducts on spinel oxides. In agreement with its computationally predicted Evac, NiCo2O4 was confirmed to have the highest DOSC and lowest T50 among the tested spinel samples.

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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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