Multiscale modeling reveals aluminum nitride as an efficient propane dehydrogenation catalyst

Multiscale modeling reveals aluminum nitride as an efficient propane dehydrogenation catalyst
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多尺度建模揭示氮化铝是一种高效的丙烷脱氢催化剂

DOI:
10.1039/d2cy02173k
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发表时间:
2023
影响因子:
5
通讯作者:
Mpourmpakis, Giannis
Mpourmpakis, Giannis
中科院分区:
化学2区
文献类型:
--
作者:
Abdelgaid, Mona;Miu, Evan V.;Kwon, Hyunguk;Kauppinen, Minttu M.;Grönbeck, Henrik;Mpourmpakis, Giannis

文献摘要

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丙烷非氧化脱氢(PDH)是一种很有前途的路线,以满足不断增长的需求,丙烯,在化学工业的重要组成部分。纤锌矿族-IIIA金属氮化物是用于PDH的潜在催化剂,具有高化学、热和机械稳定性以及可活化烷烃的C-H键的固有刘易斯酸碱性质。在这里,我们调查的催化行为的原始(AlN)和镓掺杂(Ga/AlN)氮化铝PDH通过协调和各种逐步机制,使用密度泛函理论(DFT)计算和微动力学模型(MKM)。在MKM中使用DFT计算研究的反应曲线,这表明在AlN和Ga/AlN上的逐步机制产生>99%的丙烯。AlN具有比Ga/AlN高大约一个数量级的活性,这是由于沿主要PDH反应途径的沿着势垒较低。总之,我们提出了氮化铝作为一种有效的脱氢催化剂转化为有价值的烯烃的低碳烷烃的潜在应用。此外,我们表明,多尺度模拟是必不可少的,以评估复杂的烷烃转化反应网络的催化行为,并获得脱氢催化剂的活性趋势。
Nonoxidative propane dehydrogenation (PDH) is a promising route to meet the steadily increasing demand for propylene, an important building block in the chemical industry. Wurtzite group-IIIA metal nitrides are potential catalysts for PDH with high chemical, thermal, and mechanical stability alongside inherent Lewis acid–base properties that can activate the C–H bond of alkanes. Herein, we investigate the catalytic behavior of pristine (AlN) and gallium-doped (Ga/AlN) aluminum nitride for PDH via concerted and various stepwise mechanisms using density functional theory (DFT) calculations and microkinetic modeling (MKM). The reaction profiles investigated with DFT calculations are used in MKM, which reveals that the stepwise mechanisms produce >99% of propylene on both AlN and Ga/AlN. AlN has approximately one order of magnitude higher activity than Ga/AlN due to lower barriers along the dominant PDH reaction pathway. In summary, we propose the potential application of AlN as an efficient dehydrogenation catalyst for the conversion of light alkanes into valuable olefins. In addition, we show that multiscale simulations are essential to evaluate the catalytic behavior of complex alkane conversion reaction networks and obtain activity trends for dehydrogenation catalysts.