(gc3568)Computational Design of Catalysts: The Control of Chemical Transformation
EMSL Project ID
3568
Abstract
The petroleum and chemical industries contribute ~$500 billion to the GNP of the U.S. These industries rely for their financial well being on their ability to produce new products by using energy-efficient, low-cost, and environmentally clean processes, with a minimal number of undesirable side products. Key ingredients in 90% of chemical manufacturing processes are catalysts. A catalyst?s role is to make a chemical reaction that produces a desired product proceed much more efficiently than it otherwise would by changing the kinetics of the process. Catalysis and catalytic processes account for nearly 20% of the U.S. GDP and nearly 20% of all industrial products. Chemical transformations in industry take a cheap feedstock (usually some type of hydrocarbon) and convert it into a higher value product by rearranging the carbon atoms and by adding functional groups to the compound. About 5 quads per year are used in the production of the top 50 chemicals in the U.S. and catalytic routes account for the production of 30 of these chemicals, consuming 3 quads. Improved catalysts can increase efficiency leading to reduced energy requirements, while increasing product selectivity and concomitantly decreasing wastes and emissions. A process yield improvement of only 10% would save 0.23 quads per year! In addition, production of the top 50 chemicals leads to almost 21 billion pounds of CO2 emitted to the atmosphere per year. Improved catalysts can help to reduce this carbon burden on the atmosphere. As new products become ever more sophisticated, the need to quickly develop new catalysts grows rapidly in importance. A fundamental understanding of chemical transformations is needed to enable scientists to address the grand challenge of the precise control of molecular processes by using catalysts. A desirable approach to catalyst design is to analyze at the molecular level exactly how catalysts function and to use this information to lead to the discovery of new systems and to optimize the design of others. Without this information, it is impossible to ?tune? the catalyst to have the desired effect. For example, even the most sophisticated experimental techniques are unable to provide the details of the chemical reactions occurring at the surface of a heterogeneous catalyst or information about how to tune a homogeneous catalyst to gain a factor of 2 to 4 in performance. Computational methods hold the key to catalyst design. Advanced characterization tools and in situ spectroscopies can provide identities and structures of reactive intermediates (and thus reaction mechanisms); time-resolved methods will provide kinetics and dynamics of elementary processes. These constitute critical benchmarks for validating computational methods. However, catalyst design will require quantitative information about transition states for critical reaction processes in catalysis. These are only accessible by computational methods. It is the marriage of theory and experiment that will lead us to quantitative design principles and methodologies. Computational chemistry is an enabling tool for addressing challenges in the optimal design of processes for controlling and enabling chemical transformations leading to processes that have high selectivity, have minimal environmental impact, and are optimal in their use of energy. We propose to use computational chemistry to address a variety of problems in catalyst science including oxidative dehydrogenation, organic oxidation chemistry and selectivity, hydrogenation of alkenes and isomerization of alkenes and alkanes, and reactions of carbohydrates. This will be done for both heterogeneous and homogeneous catalytic systems.
Project Details
Project type
Capability Research
Start Date
2003-09-30
End Date
2006-10-01
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members
Related Publications
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Zhai HJ, B Kiran, L Cui, X Li, DA Dixon, and LS Wang. 2004. "Electronic Structure and Chemical Bonding in MOn- and MOn Clusters (M=Mo, W; n=3-5): A Photoelectron Spectroscopy and ab Initio Study." Journal of the American Chemical Society 126(49):16134-16141 . doi: 10.1021/ja046536s
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Zhai HJ, X Huang, T Waters, XB Wang, RA O'Hair, AG Wedd, and LS Wang. 2005. "Photoelectron Spectroscopy of Doubly and Singly Charged Group VIB Dimetalate Anions: M2O72-, MM'072-, and M207- (M, M'=Cr, Mo, W)." Journal of Physical Chemistry A 109(46):10512-10520. doi:10.1021/jp055122y
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