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Title: Experimental data based combinatorial kinetic simulations for predictions of synergistic catalyst mixtures

Abstract

Physical mixtures of catalysts can sometimes show synergistic activity which exceeds the sum of the activities of the same amount of catalysts employed separately. However, identifying such synergistic mixtures is non-trivial. Not all mixtures display synergy, and the number of combinations that are possible (even for binary mixtures of equal portions) scales very rapidly, and thus they would be costly to screen experimentally. In this work, we show that it is possible to predict synergistic mixtures using combinatorial kinetic simulations based on experimental data collected on individual catalysts. The data was first collected for conditions-of-interest under low conversions (also called near differential conditions, such that each condition approximates a small volume in a non-differential conditions reactor) to build a library of kinetic models (one model for each catalyst). This data was then used for combinatorial kinetic simulations of non-differential reactor scale conversions, and successfully predicted the qualitative behavior of two synergistic physical mixtures. The capability was utilized in the present work in the context of converting CO, C 3H 6, and NO species in simulated car exhaust, but is a general approach. We provide equations to estimate the costs of the screening method as well as the combinatorial kinetics methodmore » and show that the costs of identifying synergistic physical mixtures become much lower with the combinatorial kinetics method even before ten catalysts of interest. The cost of using prediction by the combinatorial kinetics method continue to become lower (relative to screening) when more catalysts are added -- and even more so if mixtures beyond two components, or consideration of multiple catalyst ratios, is of interest.« less

Authors:
 [1];  [2];  [3];  [2]; ORCiD logo [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division; Grinnell College, Grinnell, IA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Applied Catalysts and Emissions Research Group
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1559670
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Catalysis Today
Additional Journal Information:
Journal Volume: 338; Journal Issue: C; Journal ID: ISSN 0920-5861
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; combinatorial; kinetics; physical mixture; synergy; fixed bed; concentration gradient

Citation Formats

Vuong, Hung, Binder, Andrew J., Sutton, Jonathan E., Toops, Todd, and Savara, Aditya. Experimental data based combinatorial kinetic simulations for predictions of synergistic catalyst mixtures. United States: N. p., 2019. Web. doi:10.1016/j.cattod.2019.04.026.
Vuong, Hung, Binder, Andrew J., Sutton, Jonathan E., Toops, Todd, & Savara, Aditya. Experimental data based combinatorial kinetic simulations for predictions of synergistic catalyst mixtures. United States. doi:10.1016/j.cattod.2019.04.026.
Vuong, Hung, Binder, Andrew J., Sutton, Jonathan E., Toops, Todd, and Savara, Aditya. Fri . "Experimental data based combinatorial kinetic simulations for predictions of synergistic catalyst mixtures". United States. doi:10.1016/j.cattod.2019.04.026.
@article{osti_1559670,
title = {Experimental data based combinatorial kinetic simulations for predictions of synergistic catalyst mixtures},
author = {Vuong, Hung and Binder, Andrew J. and Sutton, Jonathan E. and Toops, Todd and Savara, Aditya},
abstractNote = {Physical mixtures of catalysts can sometimes show synergistic activity which exceeds the sum of the activities of the same amount of catalysts employed separately. However, identifying such synergistic mixtures is non-trivial. Not all mixtures display synergy, and the number of combinations that are possible (even for binary mixtures of equal portions) scales very rapidly, and thus they would be costly to screen experimentally. In this work, we show that it is possible to predict synergistic mixtures using combinatorial kinetic simulations based on experimental data collected on individual catalysts. The data was first collected for conditions-of-interest under low conversions (also called near differential conditions, such that each condition approximates a small volume in a non-differential conditions reactor) to build a library of kinetic models (one model for each catalyst). This data was then used for combinatorial kinetic simulations of non-differential reactor scale conversions, and successfully predicted the qualitative behavior of two synergistic physical mixtures. The capability was utilized in the present work in the context of converting CO, C3H6, and NO species in simulated car exhaust, but is a general approach. We provide equations to estimate the costs of the screening method as well as the combinatorial kinetics method and show that the costs of identifying synergistic physical mixtures become much lower with the combinatorial kinetics method even before ten catalysts of interest. The cost of using prediction by the combinatorial kinetics method continue to become lower (relative to screening) when more catalysts are added -- and even more so if mixtures beyond two components, or consideration of multiple catalyst ratios, is of interest.},
doi = {10.1016/j.cattod.2019.04.026},
journal = {Catalysis Today},
number = C,
volume = 338,
place = {United States},
year = {2019},
month = {11}
}

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