首页|Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning

Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning

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Catalytic hydrogenation is the key measure to remove traces of acetylene in ethylene in the petroleum industry. Herein we report a highly selective and stable nanocatalyst, Pd1Ag3 supported on rutile-TiO2 (r-TiO2) annealed at unusually high temperatures (>750 °C), which can purify ethylene mixed with 1% of acetylene at 97.2% selectivity and 100% acetylene conversion below 100 °C. The selectivity is more than 10% higher than that in our previous work. This advance is achieved by a rational catalyst search featuring machine learning to correlate catalyst synthesis conditions with the catalyst performance and a large-scale machine-learning atomic simulation for disclosing composite atomic structures at high temperatures. We show that Pd1Ag3 alloy crystal nanoparticles form until 727 °C and the alloy nanoparticles grow epitaxially on r-TiO2(110) via its {111} facets. The maximum exposure of the alloy {111} surface is the key to the highest selectivity among the different supports tested, as confirmed by high-resolution characterization experiments and microkinetics simulations. Our results demonstrate the power of multiscale machine-learning tools in guiding the catalyst design and clarifying the atomic nature in complex heterogeneous catalysis.

acetylene hydrogenationPd1Ag3r-TiO2machine learning simulationrandom forestSSW-NN

Lin Chen、Xiao-Tian Li、Sicong Ma

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Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, People's Republic of China

Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China

2022

10.1021/acscatal.2c04379

ACS catalysis

ACS catalysis

EI
ISSN：2155-5435
年,卷(期)：2022.12(24)
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