Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

Medford, Andrew J.; Lausche, Adam C.; Abild‐Pedersen, Frank; Temel, Burcin; Schjødt, Niels Christian; Nørskov, Jens K.; Studt, Felix

doi:10.1007/s11244-013-0169-0

Cited by 191 publications

(221 citation statements)

References 44 publications

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“…Studies such as micro-kinetics together with theoretical calculations can be helpful for a fundamental understanding of the reaction mechanism. Very interesting reports on this topic have been published in recent years [32,33]. The inclusion of the WGSR and its effect on the product distribution can be interesting to study and compare with the results obtained in the present work.…”

Section: Interpretation Of the Catalytic Performance Of Rh/mcm-41 Comsupporting

confidence: 56%

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

et al. 2015

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Abstract:The thermochemical processing of biomass is an alternative route for the manufacture of fuel-grade ethanol, in which the catalytic conversion of syngas to ethanol is a key step. The search for novel catalyst formulations, active sites and types of support is of current interest. In this work, the catalytic performance of an Rh/MCM-41 catalyst has been evaluated and compared with a typical Rh/SiO2 catalyst. They have been compared at identical reaction conditions (280 °C and 20 bar), at low syngas conversion (2.8%) and at same metal dispersion (H/Rh = 22%). Under these conditions, the catalysts showed different product selectivities. The differences have been attributed to the concentration of water vapor in the pores of Rh/MCM-41. The concentration of water vapor could promote the water-gas-shift-reaction generating some extra carbon dioxide and hydrogen, which in turn can induce side reactions and change the product selectivity. The extra hydrogen generated could facilitate the hydrogenation of a C2-oxygenated intermediate to ethanol, thus resulting in a higher ethanol selectivity over the Rh/MCM-41 catalyst as compared to the typical Rh/SiO2 catalyst; 24% and 8%, respectively. The catalysts have been characterized, before and after reaction, by N2-physisorption, X-ray photoelectron OPEN ACCESSCatalysts 2015, 5 1738 spectroscopy, X-ray diffraction, H2-chemisorption, transmission electron microscopy and temperature programmed reduction.

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Section: Interpretation Of the Catalytic Performance Of Rh/mcm-41 Comsupporting

confidence: 56%

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

et al. 2015

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“…The first extension is the possibility of multiple sites. There are many examples where intermediates preferentially adsorb at different sites, leading to non-competitive adsorption [51][52][53]. In these cases it is desirable to enforce site conservation differently for different adsorbates:…”

Section: Solving the Kinetic Modelmentioning

confidence: 99%

CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends

et al. 2015

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Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters-reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few ''descriptors'' upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem-preferably to 1 or 2 descriptors-thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The ''CatMAP'' Python module seeks to standardize and automate many of the mathematical routines necessary to move from ''descriptor space'' to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A ''reaction model'' can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction. Graphical Abstract

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“…[3][4][5] Supported rhodium is an attractive model system, since product selectivity is tunable in a wide range through the selection of specic supports 6,7 or the addition of promoters.…”

Section: Introductionmentioning

confidence: 99%

Insights into structure and dynamics of (Mn,Fe)O_x-promoted Rh nanoparticles

et al. 2018

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The mutual interaction between Rh nanoparticles and manganese/iron oxide promoters in silica-supported Rh catalysts for the hydrogenation of CO to higher alcohols was analyzed by applying a combination of integral techniques including temperature-programmed reduction (TPR), X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS) and Fourier transform infrared (FTIR) spectroscopy with local analysis by using high angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) in combination with energy dispersive X-ray spectroscopy (EDX). The promoted catalysts show reduced CO adsorption capacity as evidenced through FTIR spectroscopy, which is attributed to a perforated core-shell structure of the Rh nano-particles in accordance with the microstructural analysis from electron microscopy. Iron and manganese occur in low formal oxidation states between 2+ and zero in the reduced catalysts as shown by using TPR and XAS. Infrared spectroscopy measured in diffuse reflectance at reaction temperature and pressure indicates that partial coverage of the Rh particles is maintained at reaction temperature under operation and that the remaining accessible metal adsorption sites might be catalytically less relevant because the hydrogenation of adsorbed carbonyl species at 523 K and 30 bar hydrogen essentially failed. It is concluded that Rh 0 is poisoned due to the adsorption of CO under the reaction conditions of CO hydrogenation. The active sites

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Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

Cited by 191 publications

References 44 publications

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends

Insights into structure and dynamics of (Mn,Fe)O_x-promoted Rh nanoparticles

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Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

Cited by 191 publications

References 44 publications

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

Synthesis of Ethanol from Syngas over Rh/MCM-41 Catalyst: Effect of Water on Product Selectivity

CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends

Insights into structure and dynamics of (Mn,Fe)Ox-promoted Rh nanoparticles

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Product

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Insights into structure and dynamics of (Mn,Fe)O_x-promoted Rh nanoparticles