As an important step toward overcoming the technical and environmental limitations of current REE processing methods, the LLNL team has patented and demonstrated a biobased, all-aqueous REE extraction and separation scheme using the REE-selective lanmodulin protein. Lanmodulin can be fixed onto porous support materials using thiol-maleimide chemistry, which can enable tandem REE purification…

LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects.  DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications. 

The essence of this invention is a method that couples network architecture using neural implicit representations coupled with a novel parametric motion field to perform limited angle 4D-CT reconstruction of deforming scenes.

LLNL researchers have developed a Li-Sn-Zn ternary alloy and its method of production.  Instead of traditional alloying techniques, the alloy was synthesized using mechanical alloying (high energy ball milling).  With high purity elemental powders of lithium, tin and zinc, LLNL researchers were able to prepare Li60Sn20Zn20 as well as Li70Sn20Zn10 nanopowders.

CMI—a DOE Energy Innovation Hub—is a public/private partnership led by the Ames Laboratory that brings together the best and brightest research minds from universities, national laboratories (including LLNL), and the private sector to find innovative technology solutions to make better use of materials critical to the success of clean energy technologies as well as develop resilient and secure…

To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…

LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…