LLNL researchers have designed and developed a novel high-density, high-channel count 3D connector that enables hundreds or thousands of nonpermanent connections within a compact footprint. The connector addresses limitations of currently used conventional approaches that were described previously, which have an artificial ceiling on the number of recording sites of modern devices of no more…

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…

Many of the disadvantages of current interface devices can be overcome with LLNL’s novel interface design, which relies on area array distribution where independent interface connector subassemblies are positioned in a planar grid.  Not only is the interface device expandable area-wise (without increasing contact force), but it could also be expanded height-wise, with multiple layers of…

Commercial fiber optic cables are the current standard for carrying optical signals in industries like communications or medical devices. However, the fibers are made of glass, which do not have favorable characteristics for applications that require flexibility and re-routing, e.g. typically brittle, limited selection of materials, dimension constraints.

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…

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…

LLNL has developed a brain-on-a-chip system with a removable cell-seeding funnel to simultaneously localize neurons from various brain regions in an anatomically relevant manner and over specific electrode regions of a MEA. LLNL’s novel, removable cell seeding funnel uses a combination of 3D printing and microfabrication that allows neurons from select brain regions to easily be seeded into…