Emily K. Schroeder, Prasanna Dasari, Muhammad Amtiaz Nadeem, Dustin Fickel and Phillip Christopher*,
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引用次数: 0
Abstract
The activity and stability of bimetallic Pt–Ir nanoparticles supported on an Al2O3/ZSM-5 mixture were investigated as a function of pretreatment and regeneration conditions for butane hydrogenolysis to ethane. Catalyst characterization by scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy before and after aging under butane hydrogenolysis conditions for 12 weeks confirmed that the bimetallic nanoparticles were resistant to sintering, coking, and bulk metal segregation. However, for catalysts that were pretreated through an initial H2 reduction, n-butane conversion decreased from 68 to 34% after 12 days on stream while maintaining ∼76% selectivity to ethane. A specific regeneration (or pretreatment) protocol was identified, involving the exposure of the oxidized catalyst to a butane and hydrogen mixture followed by post-reduction, which recovered the catalyst activity and enhanced catalyst stability such that n-butane conversion decreased <5% after 6 days on stream. The influence of various treatments on the structure and surface composition of the bimetallic nanoparticles was hypothesized based on analysis of in situ and cryogenic CO probe-molecule diffuse reflectance infrared Fourier transform spectroscopy measurements. Based on this analysis, it was inferred that high-temperature H2 treatment of oxidized catalysts resulted in intraparticle segregation into a Pt shell and Ir core that was detrimental to long-term catalyst performance. The core–shell structure was reversible upon catalyst oxidation in O2, forming an oxidized Ir (IrOx) shell and Pt core. Treatment of the oxidized catalyst with a butane and H2 mixture deposited CO and hydrocarbon adsorbates on the IrOx shell, which stabilized Ir on the nanoparticle surface, even under reductive conditions. Post-reduction in H2 restored the initial n-butane conversion with improved catalyst stability due to the adsorbate-stabilized, Ir-enriched surface. Therefore, carefully designed pretreatment protocols that deposit stable spectator adsorbates are presented as a valuable tool for controlling the surface composition of bimetallic nanoparticles under reaction conditions to improve their catalytic performance.
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)