Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate
(217)
- Fusion and Fission Energy and Science Directorate (21)
- Information Technology Services Directorate (2)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities
(27)
Researcher
- Vivek Sujan
- Amit Shyam
- Beth L Armstrong
- Peeyush Nandwana
- Ying Yang
- Alex Plotkowski
- Brian Post
- Jun Qu
- Kyle Kelley
- Omer Onar
- Rama K Vasudevan
- Rangasayee Kannan
- Ryan Dehoff
- Sudarsanam Babu
- Yong Chae Lim
- Adam Siekmann
- Adam Willoughby
- Alice Perrin
- Blane Fillingim
- Bruce A Pint
- Christopher Ledford
- Corson Cramer
- David S Parker
- Edgar Lara-Curzio
- Erdem Asa
- James A Haynes
- Lauren Heinrich
- Meghan Lamm
- Michael Kirka
- Rishi Pillai
- Rob Moore II
- Sergei V Kalinin
- Shajjad Chowdhury
- Stephen Jesse
- Steve Bullock
- Steven J Zinkle
- Subho Mukherjee
- Sumit Bahl
- Thomas Feldhausen
- Tomas Grejtak
- Yanli Wang
- Yousub Lee
- Yutai Kato
- Zhili Feng
- Adam Stevens
- An-Ping Li
- Andres Marquez Rossy
- Andrew F May
- Andrew Lupini
- Anton Ievlev
- Ben Garrison
- Ben Lamm
- Bishnu Prasad Thapaliya
- Bogdan Dryzhakov
- Brad Johnson
- Brandon Johnston
- Brian Sales
- Bryan Lim
- Charles Hawkins
- Christopher Fancher
- Costas Tsouris
- David J Mitchell
- Dean T Pierce
- Eric Wolfe
- Ethan Self
- Frederic Vautard
- Gabriel Veith
- Gerry Knapp
- Glenn R Romanoski
- Gordon Robertson
- Govindarajan Muralidharan
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Hsin Wang
- Huixin (anna) Jiang
- Hyeonsup Lim
- Isabelle Snyder
- James Klett
- Jamieson Brechtl
- Jay Reynolds
- Jeff Brookins
- Jewook Park
- Jian Chen
- Jiheon Jun
- Jong K Keum
- Jordan Wright
- Jovid Rakhmonov
- Kai Li
- Kashif Nawaz
- Kevin M Roccapriore
- Khryslyn G Araño
- Liam Collins
- Marie Romedenne
- Marm Dixit
- Marti Checa Nualart
- Matthew Brahlek
- Matthew S Chambers
- Maxim A Ziatdinov
- Mike Zach
- Mina Yoon
- Nancy Dudney
- Nedim Cinbiz
- Neus Domingo Marimon
- Nicholas Richter
- Nidia Gallego
- Olga S Ovchinnikova
- Ondrej Dyck
- Patxi Fernandez-Zelaia
- Peter Wang
- Priyanshi Agrawal
- Radu Custelcean
- Roger G Miller
- Rose Montgomery
- Saban Hus
- Sarah Graham
- Sergiy Kalnaus
- Steven Randolph
- Sunyong Kwon
- Thomas R Muth
- Tim Graening Seibert
- Tolga Aytug
- Trevor Aguirre
- Venugopal K Varma
- Weicheng Zhong
- Wei Tang
- William Peter
- Xiang Chen
- Yan-Ru Lin
- Yiyu Wang
- Yongtao Liu
- Yukinori Yamamoto

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

A novel method that prevents detachment of an optical fiber from a metal/alloy tube and allows strain measurement up to higher temperatures, about 800 C has been developed. Standard commercial adhesives typically only survive up to about 400 C.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.