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Can I read Efficient Screening of Metal Promoters of Pt Catalysts for C–H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study on EtoBox?

Efficient Screening of Metal Promoters of Pt Catalysts for C–H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study by Nuodan Zhou; Wen Liu; Faheem Jan; ZhongKang Han; Bo Li is a Engineering article available to read on EtoBox.

What is Efficient Screening of Metal Promoters of Pt Catalysts for C–H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study about?

Platinum-based materials are the most widely used catalysts in propane direct dehydrogenation, which could achieve a balanced activity between both propane conversion and propene formation. One of the core issues of Pt catalysts is how to efficiently activate the strong C–H bond. It has been suggested that adding second metal promoters could greatly solve this problem. In the current work, first-principles calculations combined with machine learning are performed in order to obtain the most promising metal promoters and identify key descriptors for control performance. The combination of three different modes of adding metal promoters and two ratios between promoters and platinum sufficiently describes the system under investigation. The activity of propane activation and the formation of propene are reflected by the increase or decrease of the adsorption energy and C–H bond activation of propane and propene after the addition of promoters. The data of adsorption energy and kinetic barriers from first-principles calculations are streamed into five machine-learning methods including gradient boosting regressor (GBR), K neighbors regressor (KNR), random forest regressor (RFR), and Ad

Who reads Efficient Screening of Metal Promoters of Pt Catalysts for C–H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study?

It is typically read by researchers, students, and practitioners in Engineering.

Author
Nuodan Zhou; Wen Liu; Faheem Jan; ZhongKang Han; Bo Li
Publisher
American Chemical Society (ACS)
Published
2023
Language
EN
Field
Engineering (Physical Sciences)

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