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ORNL scientists used molecular dynamics simulations, exascale computing, lab testing and analysis to accelerate the development of an energy-saving method to produce nanocellulosic fibers.

A team led by scientists at ORNL identified and demonstrated a method to process a plant-based material called nanocellulose that reduced energy needs by a whopping 21%, using simulations on the lab’s supercomputers and follow-on analysis.

Jay Tiley inspects a hydroelectric runner from TVA’s Cherokee Dam

ORNL is working with industry partners to develop a technique that combines 3D printing and conventional machining to produce large metal parts for clean energy applications. The project, known as Rapid Research on Universal Near Net Shape Fabrication Strategies for Expedited Runner Systems, or Rapid RUNNERS, recently received $15 million in funding from DOE. 

Bryan Maldonado

As a mechanical engineer in building envelope materials research at ORNL, Bryan Maldonado sees opportunities to apply his scientific expertise virtually everywhere he goes, from coast to coast. As an expert in understanding how complex systems operate, he’s using machine learning methods to control the process and ultimately optimize performance. 

Infuse logo

ORNL is the lead partner on five research collaborations with private fusion companies in the 2024 cohort of the Innovation Network for FUSion Energy, or INFUSE, program. These collaborative projects are intended to resolve technical hurdles and develop enabling technologies to accelerate fusion energy research in the private sector.

ORNL researchers Tom Beck, left, Sarp Oral and Rafael Ferreira da Silva have proposed a strategy for integrating classical supercomputers such as Frontier, the world’s first exascale computer, with the emerging field of quantum computing.

A study by more than a dozen scientists at the Department of Energy’s Oak Ridge National Laboratory examines potential strategies to integrate quantum computing with the world’s most powerful supercomputing systems in the pursuit of science.

A portrait of John Sanseverino.

John joined the MPEX project in 2019 and has served as project manager for several organizations within ORNL.

Man is leaning against the window, arms crossed in a dark navy button up.

Brian Sanders is focused on impactful, multidisciplinary science at Oak Ridge National Laboratory, developing solutions for everything from improved imaging of plant-microbe interactions that influence ecosystem health to advancing new treatments for cancer and viral infections. 

Researcher Brittany Rodriguez works with an ORNL-developed Additive Manufacturing/Compression Molding system that 3D prints large-scale, high-volume parts made from lightweight composites. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Brittany Rodriguez never imagined she would pursue a science career at a Department of Energy national laboratory. However, after some encouraging words from her mother, input from key mentors at the University of Texas Rio Grande Valley, or UTRGV, and a lot of hard work, Rodriguez landed at DOE’s Manufacturing Demonstration Facility, or MDF, at Oak Ridge National Laboratory.

This photo is of four men standing in front of a wall of monitors that are showing a tree looking image.

To better predict long-term flooding risk, scientists at the Department of Energy’s Oak Ridge National Laboratory developed a 3D modeling framework that captures the complex dynamics of water as it flows across the landscape. The framework seeks to provide valuable insights into which communities are most vulnerable as the climate changes, and was developed for a project that’s assessing climate risk and mitigation pathways for an urban area along the Southeast Texas coast.

Man in blue suit and blue and white button down with brown air and brown facial hair smiles for a photo with a green and teal background. Plus a quote

As a data scientist, Daniel Adams uses storytelling to parse through a large amount of information to determine which elements are most important, paring down the data to result in the most efficient and accurate data set possible.