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.
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LLNL researchers have developed a method to enhance the performance of polyelectrolyte membranes by using a humidity-controlled crosslinking process which can be applied to precisely adjust the water channels of the membrane.
LLNL researchers have developed a fabrication process for creating 3D random interdigitated architectures of anodes and cathodes, eliminating the need for a membrane to separate them. This approach is similar to the repeating interdigitated multi-electrode architectures that also were developed at LLNL.
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.
Improving the active material of the Zn anode is critical to improving the practicality of Zn-MnO2 battery technology. LLNL researchers have developed a new category of 3D structured Zn anode using a direct-ink writing (DIW) printing process to create innovative hierarchical architectures. The DIW ink, which is a gel-based mixture composed of zinc metal powder and organic binders, is…
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…