How van der Waals Approximation Methods Affect Activation Barriers of Cyclohexene Hydrogenation over a Pd Surface

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2022-09-09 DOI:10.1021/acsengineeringau.2c00031
Jacob Crouch, Tong Mou, Gengnan Li, Daniel Resasco and Bin Wang*, 
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Abstract

Inclusion of van der Waals (vdW) interactions in density functional theory (DFT) calculations improves the accuracy of the calculations of molecular structures, solid structures, and molecular adsorption configuration and energy. However, it remains unclear how vdW approximations affect calculations of activation barriers of surface reactions, which is valuable for evaluating the reaction kinetics. In this work, we choose a prototype reaction─cyclohexene hydrogenation on a Pd surface─as an example to compare different approaches to include vdW interactions in the calculation of activation barriers of surface elementary steps. We find that the adsorption of cyclohexene and desorption of the product, cyclohexane, are very sensitive to the approaches used to incorporate vdW interactions, while the intrinsic barrier of hydrogenation only varies by about 10%. As a result, the apparent activation barrier also varies to a large extent (from −1.90 to 0.28 eV). The rate-determining transition state was found to be the first hydrogenation step, independent of the vdW approximation used. These calculations indicate that the comparison of intrinsic (true) activation barriers between experimentally measured activation barriers and calculated values is more straightforward, while the comparison for the apparent activation energy may be less reliable. Therefore, simultaneous measurement of intrinsic and apparent activation barriers could serve as a potential way to benchmark the most reliable vdW approximation for molecular adsorption and reaction.

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范德华近似方法如何影响Pd表面环己烯加氢的激活势垒
在密度泛函理论(DFT)计算中加入范德华(vdW)相互作用提高了分子结构、固体结构、分子吸附构型和能量计算的准确性。然而,目前尚不清楚vdW近似如何影响表面反应的激活势垒计算,这对评估反应动力学有价值。在这项工作中,我们选择了一个原型反应──环己烯在Pd表面上的加氢──作为一个例子,比较了在计算表面基本步骤的激活势垒时包括vdW相互作用的不同方法。我们发现环己烯的吸附和产物环己烷的解吸对vdW相互作用的方法非常敏感,而氢化的本征势垒仅变化约10%。因此,表观激活势垒也有很大的变化(从- 1.90到0.28 eV)。发现决定速率的过渡态是第一个氢化步骤,与所使用的vdW近似无关。这些计算表明,实验测量的激活势垒与计算值之间的本征(真)激活势垒的比较更为直接,而表观活化能的比较可能不太可靠。因此,同时测量本征和表观激活势垒可以作为一种潜在的方法来基准最可靠的vdW近似分子吸附和反应。
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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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