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Automated Generation and Analysis of the Complex Catalytic Reaction Network of Ethanol Synthesis from Syngas on Rh(111)

机译:从RH(111)中合成气乙醇合成复合催化反应网络的自动发电和分析(111)

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Reactions on the surface of catalysts are rather complex, and many possible reaction pathways and intermediates are involved. We here propose a method that is able to automatically generate a catalytic reaction network and identify the preferred reaction pathway with determined uncertainty. Taking syngas conversion to ethanol on Rh(111) as an example, a reaction network consisting of 95 elementary steps was generated. Using energies calculated with an ensemble of 2000 functionals, the occurrence frequency of different ethanol formation pathways was obtained through pruning of the reaction network with mean-field microkinetic modeling. We found that CHCO is the most important reaction intermediate for ethanol formation with the highest confidence, even at varied temperatures and pressures. The transition state of CH3CH2O hydrogenation, i.e. CH3CH2O-H, possesses the highest possibility to be rate-controlling. CO has the highest possibility to be the surface dominant species at all the temperatures and pressures considered. The method developed in the current work substantially reduces the complexity of identifying the mechanism of catalytic reactions and shows great potential in expediting future catalyst design.
机译:催化剂表面的反应是相当复杂的,并且涉及许多可能的反应途径和中间体。我们在此提出一种能够自动产生催化反应网络的方法,并确定具有确定的不确定性的优选反应途径。将合成气转化为RH(111)的乙醇作为示例,产生由95个基本步骤组成的反应网络。使用用2000个功能的集合计算的能量,通过用平均场微蓄电图灌注来获得不同乙醇形成途径的发生频率。我们发现Chco是乙醇形成最重要的反应中间体,即使在不同的温度和压力下也是最大的置信度。 CH3CH2O氢化的过渡状态,即CH 3 CH 2 O-H具有对速率控制的最高可能性。 CO具有最高的可能性在所有温度和压力下都是表面优势物种。在当前工作中开发的方法基本上降低了鉴定催化反应机制的复杂性,并在加速未来催化剂设计方面表现出极大的潜力。

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