Enhancing Americium-241 Production: New Resins Boost Efficiency and Safety

Researchers at Los Alamos National Laboratory (LANL) have developed innovative resin materials that improve the recovery of the radioactive isotope americium-241 (Am-241) from plutonium waste. This breakthrough offers a safer and more efficient method for producing Am-241, a vital isotope used in smoke detectors, medical devices, and nuclear batteries.

Americium Sample

Americium-241: A Critical Isotope

Am-241, with a half-life of 432 years, is essential in a range of applications, from household smoke detectors to advanced nuclear batteries for space missions. However, global production is limited to a handful of suppliers. As demand grows, researchers have been seeking ways to make the extraction process more robust and scalable.

The Challenge of Harsh Extraction Conditions

Recovering Am-241 involves resin-filled columns exposed to extreme radiation and strong acids. Traditional resins degrade under these conditions, leading to reduced yields and increased worker exposure to harmful radiation. Recognizing these limitations, LANL scientists evaluated multiple resin types to identify materials that could withstand such harsh environments.

The study revealed a group of advanced resins that demonstrated remarkable resilience. These resins not only resisted chemical and radiological damage but also showed superior performance in capturing Am-241, even with varying feedstocks and larger batch sizes.

Implications for Production and Safety

By adopting these improved resins, producers can enhance the efficiency of Am-241 recovery while ensuring better protection for workers. This innovation supports the diversification of commercial supplies and strengthens U.S. production capabilities, contributing to the reliability of critical isotopes for both civilian and defense applications.

“Industrial use of the improved-performance resins for Am-241 extraction chromatography will increase production efficiency and reduce the amount of harmful radiation workers are exposed to.”
— Los Alamos National Laboratory (LANL)

Read the original article here: https://www.azom.com/news.aspx?newsID=64726

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

#Americium241 #NuclearMaterials #Radioisotopes #MaterialsScience #QuantumServerNetworks #AdvancedResins #RadiationSafety #PWmat

Comments

Post a Comment

Popular posts from this blog

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

OMol25: A Record-Breaking Dataset Set to Revolutionize AI in Computational Chemistry

Image
Published on: Quantum Server Networks | Date: May 2025 In an era where artificial intelligence is reshaping every scientific frontier, a groundbreaking resource named Open Molecules 2025 (OMol25) has just been launched. This record-breaking dataset is poised to radically transform the way researchers model and simulate molecular interactions, accelerating innovation in materials science, biochemistry, and clean energy. Berkeley Lab News Center reports that the dataset, jointly developed by Meta’s Fundamental AI Research (FAIR) lab and the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, encompasses over 100 million high-fidelity 3D molecular snapshots . These were generated using Density Functional Theory (DFT), a quantum mechanical modeling method renowned for its precision but traditionally limited by massive computational demands. What Makes OMol25 So Important? DFT simulations have long been a gold standard for predicting how atoms behave, includ...
Read more