An energy economy fueled by renewable resources will require significant improvement in existing materials for energy conversion and storage. Optimization of battery and fuel cell technologies requires a detailed understanding of the underlying physics and the associated limiting processes; in many cases this knowledge is sorely lacking. As a result, current practice in the fabrication of functional materials and devices follows a trial-and-error approach closer to a magic recipe than to scientific practice.
For example, today’s lithium ion batteries, as well as future energy storage and conversion such as multi-valent intercalation, fuel cells, Li-air and Li-S batteries are dependent on complex solid-liquid and interfacial reactions. Knowledge of how the solid electrode materials behave may be available on an atomistic level, but that knowledge is rarely transferred to higher length-scale modeling or device optimization. There is an urgent need to develop new models at the continuum length-scale and to develop efficient multiscale modeling and computational techniques. Indeed, feed-back between length scales is virtually non-existent, and the electrode making of today follows largely the same slurry and coating procedure irrespective of the inherent properties (anisotropy, phase behavior etc) of the electrode material.
At the same time, highly engineered materials, such as porous networks or nano-materials, play increasingly important roles in the improved performance of energy conversion devices. Porous network structures are utilized in polymer electrolyte membrane fuel cells, redox flow batteries, bulk hetero-junction or dye-sensitized solar cells, super-capacitors, and as separators for lithium ion batteries. These applications demand charge separators and high surface area catalyst layers whose robust network structures segregate and selectively transport ions and other charged entities while inhibiting back reactions and parasitic recombination. Many of the components of these devices are created in a casting process, relying upon phase separation to obtain the desired morphologies. Optimization of these complex materials requires predictive models for the impact of dispersion of functional groups, varying solvent polarity and volatility, and external fields on the resultant nano-morphology. However overcoming the time and length scale dichotomy inherent in casting processes requires novel multiscale modeling approaches.
It is the goal of this workshop to bring together mathematicians, physicists, computer scientists, materials scientists and engineers who work in the area of batteries and fuel cells to spark collaborations across disciplines and seed new interdisciplinary research directions. We expect this workshop will attract junior as well as senior participants. This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
(Massachusetts Institute of Technology)
Graeme Henkelman (University of Texas at Austin, Department of Chemistry)
Kristin Persson (Lawrence Berkeley Laboratory, EETD)
Keith Promislow (Michigan State University, Mathematics)