Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (26)
- Computing and Computational Sciences Directorate (38)
- Energy Science and Technology Directorate (223)
- Fusion and Fission Energy and Science Directorate
(24)
- Isotope Science and Enrichment Directorate (7)
- National Security Sciences Directorate (20)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (135)
- User Facilities (27)
- (-) Information Technology Services Directorate (3)
Researcher
- Hongbin Sun
- Annetta Burger
- Carter Christopher
- Chance C Brown
- Debangshu Mukherjee
- Debraj De
- Gautam Malviya Thakur
- Ilias Belharouak
- James Gaboardi
- Jason Jarnagin
- Jesse McGaha
- Josh Michener
- Kevin Spakes
- Kevin Sparks
- Liangyu Qian
- Lilian V Swann
- Liz McBride
- Mark Provo II
- Md Inzamam Ul Haque
- Olga S Ovchinnikova
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Rob Root
- Ruhul Amin
- Sam Hollifield
- Serena Chen
- Thien D. Nguyen
- Todd Thomas
- Vishaldeep Sharma
- Xiuling Nie

In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

We tested 48 diverse homologs of SfaB and identified several enzyme variants that were more active than SfaB at synthesizing the nylon-6,6 monomer.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.