LLNL researchers have developed additive manufactured fuel targets for IFE.  They have been successful in using TPL to fabricate low density (down to 60 mg/cm3) and low atomic number (CHO) polymeric foams for potential targets, and some have been tested at the OMEGA Laser Facility. With TPL, LLNL researchers have also been able to fabricate a full fuel capsule with diameter of ~ 5mm or…

LLNL researchers developed a novel strategy that involves material transformations such as oxidation, nitridation, or carbonization. In one embodiment, copper is heated under ambient conditions resulting in its surface being oxidized and turned into copper oxide, where a new material (e.g., copper oxide) is developed via transformation (e.g., oxidation) without additional addition deposition…

This invention configures multiple spherical substrate targets to roll independently of one another.  The spheres’ rolling motion is deliberately randomized to promote uniform coating while eliminating the interaction (rubbing, sliding) of adjacent spheres that is present in conventional sphere coating designs. The devices’ novel structure features enable the collimation of depositing…

This invention takes advantage of the high water-solubility of key NIF KDP crystal optics and uses water as an etchant to remove surface defects and improve the laser induced damage threshold. Since pure water etches KDP too fast, this invention is to disperse water as nanosized droplets in a water-in-oil micro-emulsion. While in a stable micro-emulsion form, the surfactant additives prevent…

This invention proposes to use laser induced melting/softening to locally reshape the form of a glass optic. The local glass densification that results induces predictable stresses that through plate deformation mechanics yield a deterministic methodology for arbitrarily reshaping an optic surface figure and wavefront without the need to remove material.

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's Slurry Stabilization Method provides a chemical means of stabilizing a polishing compound in suspension at working concentrations without reducing the rate of material removal. The treated product remains stable for many months in storage.