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Filtered by Catalysis, Nuclear Nonproliferation, and Waste-to-Energy and Products
SEPTEMBER 17, 2020
Web Feature

Not Your Average Refinery

In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.

Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions

ML and AI

Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions

August 25, 2020
August 25, 2020
Highlight

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.

Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:

  • Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
  • Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
  • Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
  • Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
  • Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC). 

The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.

View full preliminary findings of the Secretary of Energy Advisory Board (SEAB) report.

For more information about PNNL’s research contributions, contact Aaron Luttman

JULY 21, 2020
Web Feature

A Remarkable Rate of Return with Catalytic Bias

A multi-institution research team found how the protein environment surrounding some enzymes can alter the direction of a cellular reaction, as well as its rate—up to six orders of magnitude—in a phenomenon referred to as catalytic bias.
JUNE 2, 2020
Web Feature

Virtual Ceremony Honors Nuclear Security Graduates

Like many graduates crossing the finish line in 2020, the National Nuclear Security Administration Graduate Fellowship Program class of 2019-2020 transitioned its closing ceremony to a virtual environment, joined by NNSA and PNNL leaders.
MARCH 16, 2020
Web Feature

Carving Out Quantum Space

The race toward the first practical quantum computer is in full stride. Scientists at PNNL are bridging the gap between today’s fastest computers and tomorrow’s even faster quantum computers.
JANUARY 10, 2020
Web Feature

Clark Recognized for Nuclear Chemistry Research

The world’s largest scientific society honored Sue B. Clark, a PNNL and WSU chemist, for contributions toward resolving our legacy of radioactive waste, advancing nuclear safeguards, and developing landmark nuclear research capabilities.

Top Ten Blendstocks for Turbocharged Gasoline Engines

Cover of Co-Optima Report

Bio-blendstocks with the potential to deliver the highest engine efficiency

October 8, 2019
October 8, 2019
Report

More efficient engines enabled by better fuels could increase the fuel economy of light duty (LD) vehicles by 10 percent beyond current technology and planned developments. This report identifies top blendstocks that can be derived from biomass and are suitable for further development and commercialization. These blendstocks are best-suited for LD gasoline, boosted spark ignition (BSI) engines. The blendstocks were identified using a fuel property basis using the BSI merit function. The merit function determines potential improvements in engine efficiency, was used to evaluate the performance of candidate bioblendstocks in blends up to 30%. Those that exceeded the efficiency of an E10 premium were included in this list. This report is aimed at biofuel researchers looking to better understand the efficiency implications of biofuels under development, as well as engine researchers who are interested in future biofuels with properties that enable more efficient engine design and operation.

The Co-Optimization of Fuels & Engines (Co-Optima) team includes experts from nine national laboratories: Argonne, Idaho, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge, Pacific Northwest, and Sandia National Laboratories and the National Renewable Energy Laboratory. The team’s expertise includes biofuel development, fuel property testing and characterization, combustion fundamentals, modeling and simulation from atomic scale to engine scale, and analysis.

Gaspar, Daniel J., West, Brian H., Ruddy, Danial, Wilke, Trenton J., Polikarpov, Evgueni, Alleman, Teresa L., George, Anthe, Monroe, Eric, Davis, Ryan W., Vardon, Derek, Sutton, Andrew D., Moore, Cameron M., Benavides, Pahola T., Dunn, Jennifer, Biddy, Mary J., Jones, Susanne B., Kass, Michael D., Pihl, Josh A., Pihl, Josh A., Debusk, Melanie M., Sjoberg, Magnus, Szybist, Jim, Sluder, C S., Fioroni, Gina, and Pitz, William J. Top Ten Blendstocks Derived From Biomass For Turbocharged Spark Ignition Engines: Bio-blendstocks With Potential for Highest Engine Efficiency. United States: N. p., 2019. Web. doi:10.2172/1567705.