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The Summit supercomputer, once the world’s most powerful, is set to be decommissioned by the end of 2024 to make way for the next-generation supercomputer. Over the summer, crews began dismantling Summit’s Alpine storage system, shredding over 40,000 hard drives with the help of ShredPro Secure, a local East Tennessee business. This partnership not only reduced costs and sped up the process but also established a more efficient and secure method for decommissioning large-scale computing systems in the future.
Nuclear physicists at the Department of Energy’s Oak Ridge National Laboratory recently used Frontier, the world’s most powerful supercomputer, to calculate the magnetic properties of calcium-48’s atomic nucleus.
John joined the MPEX project in 2019 and has served as project manager for several organizations within ORNL.
The award was given in “recognition of his lifelong leadership in fusion technology for plasma fueling systems in magnetically confined fusion systems.”
A collection of seven technologies for lithium recovery developed by scientists from ORNL has been licensed to Element3, a Texas-based company focused on extracting lithium from wastewater produced by oil and gas production.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
Research performed by a team, including scientists from ORNL and Argonne National Laboratory, has resulted in a Best Paper Award at the 19th IEEE International Conference on eScience.
This summer, ORNL welcomed more than 500 students to campus through the lab’s range of internship programs, which are offered in areas such as biology, national security and computing.
Researchers from institutions including ORNL have created a new method for statistically analyzing climate models that projects future conditions with more fidelity.