Argonne National Laboratory
Canvas Category Consultancy : Research : National
Argonne is a multidisciplinary science and engineering research center, where talented scientists and engineers work together to answer the biggest questions facing humanity, from how to obtain affordable clean energy to protecting ourselves and our environment. Ever since we were born out of the University of Chicago’s work on the Manhattan Project in the 1940s, our goal has been to make an impact — from the atomic to the human to the global scale. The laboratory works in concert with universities, industry, and other national laboratories on questions and experiments too large for any one institution to do by itself. Through collaborations here and around the world, we strive to discover new ways to develop energy innovations through science, create novel materials molecule-by-molecule, and gain a deeper understanding of our planet, our climate, and the cosmos. Surrounded by the highest concentration of top-tier research organizations in the world, Argonne leverages its Chicago-area location to lead discovery and to power innovation in a wide range of core scientific capabilities, from high-energy physics and materials science to biology and advanced computer science.
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NREL To Lead New Lab Consortium To Enable High-Volume Manufacturing of Electrolyzers and Fuel Cells
The Roll-to-Roll (R2R) Consortium is a new national laboratory consortium with a mission to advance efficient, high-throughput, and high-quality manufacturing methods and processes to accelerate domestic manufacturing and reduce the cost of durable, high-performance proton exchange membrane fuel cell and electrolyzer systems.
The R2R Consortium is led by the National Renewable Energy Laboratory (NREL) and includes Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory, and Sandia National Laboratories.
High-throughput manufacturing of fuel cells and water electrolyzers is critical for achieving widespread deployment of low-cost, clean hydrogen technologies. Roll-to-roll manufacturing of materials can increase efficiency, reduce material waste, and improve cost, but there are challenges related to materials synthesis, coating, drying, and quality control that need to be addressed to scale up these processes for industry adoption.
Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solution that not only identifies faults but also provides clear, understandable explanations of their causes and implications. The system’s efficacy is demonstrated through application to a molten salt facility, showcasing its ability to elucidate the connections between diagnosed faults and sensor data, answer operator queries, and evaluate historical sensor anomalies. Our approach underscores the importance of merging model-based diagnostics with advanced AI to improve the reliability and transparency of autonomous systems.
Machine-Learning-Enhanced Simulation Could Reduce Energy Costs in Materials Production
Thanks to a new computational effort being pioneered by the U.S. Department of Energy’s (DOE) Argonne National Laboratory in conjunction with 3M and supported by the DOE’S High Performance Computing for Energy Innovation (HPC4EI) program, researchers are finding new ways to dramatically reduce the amount of energy required for melt blowing the materials needed in N95 masks and other applications.
Currently, the process used to create a nozzle to spin nonwoven materials produces a very high-quality product, but it is quite energy intensive. Approximately 300,000 tons of melt-blown materials are produced annually worldwide, requiring roughly 245 gigawatt-hours per year of energy, approximately the amount generated by a large solar farm. By using Argonne supercomputing resources to pair computational fluid dynamics simulations and machine-learning techniques, the Argonne and 3M collaboration sought to reduce energy consumption by 20% without compromising material quality.
Because the process of making a new nozzle is very expensive, the information gained from the machine-learning model can equip material manufacturers with a way to narrow down to a set of optimal designs. ”Machine-learning-enhanced simulation is the best way of cheaply getting at the right combination of parameters like temperatures, material composition, and pressures for creating these materials at high quality with less energy,” Blaiszik said.
A New Way to Discover a Reaction that Causes Cracks in Concrete
One phenomenon that shortens the life of concrete buildings and structures is the alkali-silica reaction (ASR). It is the reaction between alkali ions found in cement and silica, the two main components of concrete, which creates a gel that absorbs water and expands, causing internal pressures to build up within the concrete. To help identify the extent of ASR, researchers at the Argonne National Laboratory have discovered a harmless way to detect it that could reduce the level of expensive testing being done. Their new method relies on electrochemical impedance spectroscopy (EIS), which measures electrical conductivity.
Recycled cathode materials enabled superior performance for lithium-ion batteries
Recycling spent lithium-ion batteries plays a significant role in alleviating the shortage of raw materials and environmental problems. However, recycled materials are deemed inferior to commercial materials, preventing the industry from adopting recycled materials in new batteries. Here, we demonstrate that the recycled LiNi1/3Mn1/3Co1/3O2 has a superior rate and cycle performance, verified by various industry-level tests. Specifically, 1 Ah cells with the recycled LiNi1/3Mn1/3Co1/3O2 have the best cycle life result reported for recycled materials and enable 4,200 cycles and 11,600 cycles at 80% and 70% capacity retention, which is 33% and 53% better than the state-of-the-art, commercial LiNi1/3Mn1/3Co1/3O2. Meanwhile, its rate performance is 88.6% better than commercial powders at 5C. From experimental and modeling results, the unique microstructure of recycled materials enables superior electrochemical performance. The recycled material outperforms commercially available equivalent, providing a green and sustainable solution for spent lithium-ion batteries.
Scientists Set to Use Social Media AI Technology to Optimize Parts for 3D Printing
“My idea was that a material’s structure is no different than a 3D image,” he explains. “It makes sense that the 3D version of this neural network will do a good job of recognizing the structure’s properties — just like a neural network learns that an image is a cat or something else.”
To see if his idea would work, Messner designed a defined 3D geometry and used conventional physics-based simulations to create a set of two million data points. Each of the data points linked his geometry to ‘desired’ values of density and stiffness. Then, he fed the data points into a neural network and trained it to look for the desired properties.
Finally, Messner used a genetic algorithm – an iterative, optimization-based class of AI – together with the trained neural network to determine the structure that would result in the properties he sought. Impressively, his AI approach found the correct structure 2,760x faster than the conventional physics simulation.
Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis
Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.