LLNL’s invention is a photopolymerizable polymer resin that consists of one or more nitrile-functional based polymers. The resin is formulated for SLA based 3D printing allowing for the production of nitrile-containing polymer components that can then be thermally processed into a conductive, highly graphitic materials. The novelty of the invention lies in (1) the photo-curable nitrile-…

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.

LLNL researchers have developed novel advanced manufactured biomimetic 3D-TPMS (triply periodic minimal surface) membrane architectures such as a 3D gyroid membrane. The membrane is printed using LLNL's nano-porous photoresist technology.  LLNL’s 3D-TPMS membranes consist of two independent but interpenetrating macropore flow channel systems that are separated by a thin nano-porous wall…

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 get the best of both worlds – the sensitivity of LC-MS with the speed of PS-MS – and a functional substrate that can maintain sample integrity, LLNL researchers looked to 3D printing.  They have patented a novel approach to create lattice spray substrates for direct ionization mass spectroscopy using 3D-printing processes.

LLNL researchers, through careful control over the chemistry, network formation, and crosslink density of the ink formulations as well as introduction of selected additives, have been successful in preparing 3D printable silicone inks with tunable material properties.  For DIW (direct in writing) applications, LLNL has a growing IP portfolio around 3D printable silicone feedstocks for…

LLNL’s method of 3D printing fiber-reinforced composites has two enabling features:

Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.

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…

Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…

Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…