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 researchers have discovered that some inexpensive and commercially available molecules used for other applications, could render certain lanthanide and actinide elements highly fluorescent. These molecules are not sold for applications involving the detection of REEs and actinides via fluorescence. They are instead used as additives in cosmetic products and/or in the pharmaceutical…

The suppressor has a series of chambers for the propellant to flow through, but unlike all traditional suppressors, the chambers are open, not closed. The propellant is not trapped. It keeps moving. We manage its unimpeded flow through the suppressor. This is the key underlying technology of our suppressor design that enables all the improvements over the 100-year old traditional designs.

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.

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…

Livermore Lab researchers have developed a tunable shaped charge which comprises a cylindrical liner commonly a metal such as copper or molybdenum but almost any solid material can be used and a surround layer of explosive in which the detonation front is constrained to propagate at an angle with respect to the charge axis.  The key to the concept is the ability to deposit a surrounding…

Livermore Lab researchers have developed a method that combines additive manufacturing (AM) with an infill step to render a final component which is energetic. In this case, AM is first used to print a part of the system, and this material can either be inert or energetic on its own. A second material is subsequently added to the structure via a second technique such as casting, melt…

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…