Effect of SO2 and SO3 Exposure to Cu-CHA on Surface Nitrate and N2O Formation for NH3–SCR

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2024-05-31 DOI:10.1021/acsengineeringau.4c00004
Joonsoo Han, Joachim D. Bjerregaard, Henrik Grönbeck, Derek Creaser, Louise Olsson
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Abstract

We report effects of SO2 and SO3 exposure on ammonium nitrate (AN) and N2O formation in Cu-CHA used for NH3–SCR. First-principles calculations and several characterizations (ICP, BET, XRD, UV–vis–DRS) were applied to characterize the Cu-CHA material and speciation of sulfur species. The first-principles calculations demonstrate that the SO2 exposure results in both (bi)sulfite and (bi)sulfate whereas the SO3 exposure yields only (bi)sulfate. Furthermore, SOx adsorption on framework-bound dicopper species is shown to be favored with respect to adsorption onto framework-bound monocopper species. Temperature-programmed reduction with H2 shows two clear reduction states and larger sulfur uptake for the SO3-exposed Cu-CHA compared to the SO2-exposed counterpart. Temperature-programmed desorption of formed ammonium nitrate (AN) highlights a significant decrease in nitrate storage due to sulfur species interacting with copper sites in the form of ammonium/copper (bi)bisulfite/sulfate. Especially, highly stable sulfur species from SO3 exposure influence the NO2–SCR chemistry by decreasing the N2O selectivity during NH3–SCR whereas an increased N2O selectivity was observed for the SO2-exposed Cu-CHA sample. This study provides fundamental insights into how SO2 and SO3 affect the N2O formation during ammonium nitrate decomposition in NH3–SCR applications, which is a very important topic for practical applications.

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二氧化硫和二氧化硫暴露于 Cu-CHA 对 NH3-SCR 表面硝酸盐和 N2O 生成的影响
我们报告了二氧化硫和二氧化硫暴露对硝酸铵(AN)和 N2O 在用于 NH3-SCR 的 Cu-CHA 中形成的影响。我们采用第一性原理计算和多种表征方法(ICP、BET、XRD、UV-vis-DRS)来表征 Cu-CHA 材料和硫的种类。第一性原理计算表明,接触二氧化硫会产生(双)亚硫酸盐和(双)硫酸盐,而接触二氧化硫则只会产生(双)硫酸盐。此外,与吸附在框架结合的单铜物种上相比,吸附在框架结合的二铜物种上的 SOx 更受青睐。用 H2 进行温度编程还原显示出两种明显的还原状态,与暴露于 SO2 的对应物相比,暴露于 SO3 的 Cu-CHA 的硫吸收量更大。对形成的硝酸铵(AN)进行温度编程解吸,结果表明,由于硫以铵/亚硫酸氢盐/硫酸铜(双)铜的形式与铜的位点相互作用,硝酸盐的储存量显著减少。特别是,暴露于 SO3 的高度稳定的硫元素会影响 NO2-SCR 化学反应,降低 NH3-SCR 过程中的 N2O 选择性,而暴露于 SO2 的 Cu-CHA 样品则会增加 N2O 选择性。这项研究为了解二氧化硫和二氧化硫如何影响 NH3-SCR 应用中硝酸铵分解过程中 N2O 的形成提供了基本见解,这对于实际应用来说是一个非常重要的课题。
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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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