INL News Release
FOR IMMEDIATE RELEASE
Aug. 22, 2022 

NEWS MEDIA CONTACTS:
Joelyn Hansen, 208-201-7650, joelyn.hansen@inl.gov 
Sarah Neumann, 208-520-1651, sarah.neumann@inl.gov  

 

It’s another winning year as three Idaho National Laboratory technologies received 2022 R&D 100 Awards.  

The competition – now in its 60th year – celebrates research and development technologies from across the public and private sectors. Winning an award is a prestigious distinction for inventors.  

Laboratories and companies from 12 countries submitted nominations in 2022, and a panel of more than 50 industry-leading experts ranked entries on technical significance, uniqueness and applicability across industry, government and academia. Typically, the U.S. Department of Energy’s national laboratories have dozens of finalists every year. 

Including this year’s winners, INL has won more than 30 R&D 100 Awards since 2005. Five INL technologies were also named as R&D 100 finalists in 2022. 

WINNING TECHNOLOGIES LED BY INL: 

Electrochemical Leach (EC-Leach) 

Description: EC-Leach provides a cost-effective, highly efficient, safe, carbon-free and remarkably simple process for solving one of our world’s biggest clean energy challenges: lithium-ion battery recycling. This technology unlocks the green energy potential of these batteries at the end of their lives by allowing extraction and recovery of critical materials. Although EC-Leach provides an answer to many complicated challenges, it is remarkable for its technological simplicity. It requires no expensive or hazardous materials, has a low operational cost, and is compatible with any lithium-ion battery chemistry. By facilitating battery recycling in a closed loop, EC-Leach enables a carbon-free transportation and manufacturing sector.

Researchers: Tedd Lister (co-principal investigator), Luis Diaz Aldana (co-principal investigator), John Klaehn, Joshua McNally, Meng Shi and Daniel Molina Montes de Oca. 

Machine Intelligence for Review and Analysis of Condition Logs and Entries (MIRACLE) 

Description: In the nuclear power industry, every issue, no matter how small, is documented in a condition report. In each plant, hundreds of these are reviewed and characterized every week by dozens of people. MIRACLE employs machine learning and natural language processing to automate this process, saving millions while improving safety. On a broader stage, although MIRACLE is intended for use in nuclear power plants, the methods developed in its creation should be valuable in any industry that requires massive volumes of documentation reviews. MIRACLE offers savings and efficiencies. 

Researchers: Ahmad Al Rashdan (principal investigator), Brian Wilcken, Cameron Krome and Kellen Giraud. 

MOSAICS 

Description: MOSAICS is a technology initiated by the Department of Defense to provide the first-ever comprehensive, integrated and automated solution to detect and prevent cyberattacks of industrial control systems. INL focused its efforts to provide scalable evaluation of commercial, off-the-shelf security solutions and test harness for initializing, launching and collecting results from cyber-resilience testing in virtual environments. INL is a partner on the MOSAICS technology with Johns Hopkins University Applied Physics Laboratory along with Sandia National Laboratories and Pacific Northwest National Laboratory. 

Researchers: Craig G. Rieger (principal investigator), Michael McCarty, Bev Novak and Roya Gordon (former INL researcher). 

FINALIST TECHNOLOGIES LED BY INL:  

Caldera 

Description: Electric vehicle ownership is expanding rapidly as the technology becomes more available and affordable. While the increase will reduce carbon emissions and the impacts of climate change, these vehicles and their associated charging infrastructure will have a huge impact on electric grids throughout the world. Caldera provides two essential services to the future of electrical vehicle charging: 1) modeling large-scale electric vehicle charging for a variety of locations and conditions, and 2) modeling charging management strategies to reduce the grid impacts of large-scale electric vehicle charging. Caldera is the missing link between transportation models, grid models and detailed charging data. 

Researchers: Don Scoffield (co-principal investigator), Timothy Pennington (co-principal investigator), John Smart, Zonggen Yi, Manoj Kumar Cebol Sundarrajan and Paden Rumsey. 

Constrained Communication Cyber Device (C3D) 

Description: Cybercriminals and nations hostile to U.S. interests have developed increasingly sophisticated ways to attack electricity distribution infrastructure. The patent-pending Constrained Communication Cyber Device (C3D) technology is an added depth of defense against cyberthreats aimed at essential electrical grid hardware called protective relays. C3D sits deep inside a utility’s network, monitoring and blocking cyberattacks before they impact relay operations. The C3D technology was licensed to Sierra Nevada Corporation in March 2022. 

Researchers: Jake Gentle (co-principal investigator) and Steve Bukowski (co-principal investigator). 

Modeling and Simulation for Target Electrical Resilience and Reliability Improvements (MASTERRI) 

Description: MASTERRI enables utility leaders to identify and prioritize electrical system repairs and upgrades to prevent cascading failures. The analysis process identifies critical vulnerabilities and the overall potential for system failure. MASTERRI delivers the most accurate information available for electrical industry decision support and is instrumental in preventing failure and building resilience. 

Researchers: Bjorn Vaagensmith (principal investigator), Kurt Vedros, Tim McJunkin, Liam Boire, Jesse Reeves, James Case, Jason Wayment, Craig Rieger, Shawn West, Courtney Otani, Pierce Russell and Carol Reid. 

RAVEN – Computational platform performing stochastic analyses   

Description: RAVEN offers a fully integrated working environment, providing everything engineers and scientists need to tackle challenging problems in an efficient and user-friendly fashion. RAVEN is a flexible and multipurpose statistical analysis framework that allows users to conveniently perform a variety of analysis, data mining and model optimization tasks. These operations are performed based on the response of complex physical models through advanced statistical sampling generation to achieve a high degree of realism and accuracy previously unattainable. RAVEN is a unique and powerful tool for risk analysis, offering capabilities not currently available in other software. 

Researchers: Diego Mandelli (co-principal investigator) and Congjian Wang (co-principal investigator). 

Robust Anode for Electrochemical in Extreme Environments (Robust Monolithic Anode) 

Description: In a world increasingly reliant on technologies with metal-based components, sustainable metal recycling methods are more essential than ever. Recycling these metals provides the dual benefit of reducing waste in landfills and minimizing the need for additional mining. While electrochemical processing is not a new technique, the graphite- and platinum-based anodes most used today lack long-term durability in the extremely hot, corrosive and oxidizing conditions that prevail in the recycling vessel, requiring frequent (and costly) replacement. The iridium anode in this technology has demonstrated long-lasting, highly efficient performance to support consumer products recycling as well as spent nuclear fuel reprocessing. 

Researchers: Prabhat Tripathy (co-principal investigator), Steven Herrmann (co-principal investigator), Dale Wahlquist, Steven Frank, James King and Ken Marsden. 

About Idaho National Laboratory
Battelle Energy Alliance manages INL for the U.S. Department of Energy’s Office of Nuclear Energy. INL is the nation’s center for nuclear energy research and development, and also performs research in each of DOE’s strategic goal areas: energy, national security, science and the environment. For more information, visit www.inl.gov. Follow us on social media: Twitter, Facebook, Instagram and LinkedIn. 

—INL-22-028 

What People Are Reading