For significant achievements in advancing battery and energy storage technologies, which are critical for the clean energy transition.
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All Corporate Fellow summaries reflect the awardee and ORNL at the time the fellowship was awarded.
2022
For leading scientific contributions in fusion energy sciences with a focus on electromagnetic plasma turbulence and on the stability and dynamics of the edge region of magnetic fusion plasmas.
2016
For his leadership in separations science and technology; for improving nuclear fuel recycling and waste removal; and for leading the development process that was instrumental in the cleanup of waste at the Savannah River Site.
2011
For his role in conceiving, designing, and implementing novel geocomputational methods to help solve a wide variety of national and global problems in energy, the environment, and national security.
For pioneering studies of the functionality of mesoporous oxides and carbons for real-world applications, ionic liquids for chemical separation and materials synthesis, and catalysis by nanomaterials.
2007
For his pioneering contributions to the study of nonequilibrium systems, quantum magnetism, and excitations in condensed matter.
2006
For contributions to high-performance networking and multiple-sensor fusion and for developing a unifying theory of information fusion.
2004
For leadership and pioneering research in the fundamental effects of radiation on a broad range of metals and ceramics applicable to fission and fusion energy systems.
2000
For distinguished research on the air/surface exchange of atmospheric trace gases and particles and their interactions with the Earth's biogeochemical cycles, and for pioneering developments in atmospheric sampling methodologies with special emphasis on the global mercury cycle.
1979
For contributions to nuclear data measurement, analysis, and applications, through determination and development of neutron-induced reaction cross sections, high-resolution neutron scattering, the nonlocal nuclear optical model, and uncertainty and covariance information