Adam C. Lausche

Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

Production of higher alcohols directly from synthesis gas is an attractive chemical process due to the high value of alcohols as fuel blends and the numerous possibilities for production of synthesis gas. Despite years of research the industrial viability of such a process is severely limited due to lack of suitable catalysts. In this work we contribute to an understanding why it has been difficult to find transition-metal higher alcohol catalysts, and point to possible strategies for discovering new active and selective catalysts. Our analysis is based on extensive density functional theory calculations to determine the energetics of ethanol formation on a series of metal (211) surfaces. The energetic information is used to construct a mean-field micro-kinetic model for the formation of ethanol via CHx–CO coupling. The kinetic model is used along with a descriptor-based analysis to gain insight into the fundamental factors determining activity and selectivity on transition-metal surfaces.

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

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

In silico search for novel methane steam reforming catalysts

This paper demonstrates a method for screening transition metal and metal alloy catalysts based on their predicted rates and stabilities for a given catalytic reaction. This method involves combining reaction and activation energies (available to the public via a web-based application ‘CatApp’) with a microkinetic modeling technique to predict the rates and selectivities of a prospective material. This paper illustrates this screening technique using the steam reforming of methane to carbon monoxide and hydrogen as a test reaction. While catalysts are already commercially available for this process, the method demonstrated in this paper is very general and could be applied to a wide range of catalytic reactions. Following the steps outlined herein, such an analysis could potentially enable researchers to understand reaction mechanisms on a fundamental level and, on this basis, develop leads for new metal alloy catalysts.

Trends in the Hydrodeoxygenation Activity and Selectivity of Transition Metal Surfaces

This paper reports the use of a combination of density functional theory and microkinetic modelling to establish trends in the hydrodeoxygenation rates and selectivites of transition metal surfaces. Biomass and biomass-derived chemicals often contain large fractions of oxygenates. Removal of the oxygen through hydrotreating represents one strategy for producing commodity chemicals from these renewable materials. Using the model developed in this paper, we predict ethylene glycol hydrodeoxygenation selectivities for transition metals that are consistent with those reported in the literature. Furthermore, the insights discussed in this paper present a framework for designing catalytic materials for facilitating these conversions efficiently.Graphical Abstract