For significant impacts to the fields of synthetic biology and biological interfaces, innovations in the use of chemistry and nanotechnology to develop a molecular mechanistic understanding of complex biological systems, and pioneering approaches in chemical imaging through integration with mass spectrometry-based detection.
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Corporate Fellow Type
Year
All Corporate Fellow summaries reflect the awardee and ORNL at the time the fellowship was awarded.
2021
For his pioneering efforts in silicon carbide–based power electronics, which have paved the way for vehicle and grid infrastructure advancements, enabling transformational achievements in wireless power transfer and electric drivetrain applications, and for the continuing significant impact his accomplishments will have on the global move toward the electrification and decarbonization of the mobility sector.
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.
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.
2001
For his internationally recognized accomplishments in high-energy physics, radiation transport, and detector and neutron target research and development.
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