(6gy) Unraveling the Chemistry of Energy Systems

The need for reliable energy sources is a constantly evolving problem with unique challenges. For the foreseeable future, much of our energy demands will still be met with hydrocarbon fuels, though the landscape of types of fuels and fuel-sources will continue to evolve. This evolution in fuel composition is currently being witnessed in the energy sector with the emergence of petroleum-derived fuels from shale oil and tar sands that have greater naphthenic content and fuels obtained from biomass with their higher content of oxygenated molecules. Each of these fuel sources/types poses challenges to infrastructure and eventual end-use applications, particularly in the transportation sector. A second challenge is that in recent years, legislature has been passed mandating significant improvement of transportation fuel efficiency, reduction of carbon-emissions, tighter pollution regulations, and increased use of bio-renewable energy sources. To meet these new regulations, new technologies are continuously being developed to increase fuel efficiency and reduce emissions, such as HCCI engines as an alternative to traditional spark ignition engines and membrane separation technology to sequester greenhouse gas emissions. Under this evolving energy landscape, it is imperative to have a detailed understanding of the complex chemistry and thermodynamics of these energy applications to accelerate the development of new green energy technologies.

Chemical kinetic modeling allows for the application of theory to interpret experimental data and elucidation of the chemical pathways essential to understanding harmful emissions formation. Kinetics models offer the practical engineer tools to improve fuel efficiency and reduce emissions, and to aid in the design and development of the next-generation of energy technologies. The development of chemical models used to model energy applications has evolved over the past several decades, both in fuel complexity and in the breadth of temperature and pressure conditions to be considered. Even simple fuels such as ethanol require models with hundreds of distinct molecules, which undergo thousands of unique elementary reactions. The complexity of these models are expanded exponentially when considering “real” fuels (e.g. diesel), which are blends of many high molecular weight species. Further complicating the problem is that energy systems operate over a wide range of conditions, requiring accurate kinetics over an incredible range of temperatures (300-2500 K) and pressures (< 0.1-100 atm). As a result of this complexity, most models to date have relied on kinetic estimations and manual tuning of kinetic rate constants to reproduce benchmark experimental data. The resultant models are often not reliable or are highly uncertain beyond the scope of the targeted benchmark experimental data, and cannot be universally applied to predict the chemistry of all energy applications.

My research will focus on the development of robust chemical kinetic models using state of the art theoretical methods to accurately unravel chemistry relevant to practical energy problems. This work will facilitate the development of new technologies to increase fuel efficiency, decrease harmful emissions, and reduce dependence on non-renewable energy sources. In my doctoral work at the University of Massachusetts Amherst, I gained extensive experience working with low-pressure flame experiments, analyzing molecular beam mass spectrometry speciation data, and building chemical kinetic models for real fuels such as hypergolic rocket fuels to solve real-world problems. I expanded my knowledge of kinetics during my postdoctoral appointment at Argonne National Laboratory where I focused on the fundamental chemistry of fuels, exploring the effects of pressure on rate constants and the effects of chemically-activated radical chemistry by combining high-level theoretical calculations of potential energy surfaces with the development of kinetic models emphasizing uncertainty reduction. These two experiences make me uniquely qualified to develop predictive kinetic models for energy applications that balance the need for immediate results, providing qualitative information on performance trends to identify targets for further research and development, with the long-term need for accurate quantitative information for specific fuels to stimulate breakthroughs in sustainable energy.

Acknowledgements

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Division of Chemical Sciences, Geosciences, and Biosciences, under contract number DE-AC02-06CH11357.