Powder atomic layer deposition process is used to coat nanopowders of host materials (e.g. yttrium aluminum garnet) with optically active neodymium organometal precursor followed by O₂/O₃ RF plasma to convert to a single layer of Nd₂O₃. The process can be repeated to build arbitrarily thick layers with custom doping profiles and followed by post-…

This invention proposes using a pulse laser configured to generate laser pulses and a controller for controlling operation of the pulse laser. The controller is further configured to control the pulse laser to cause the pulse laser to generate at least one of the laser pulses with a spatiotemporally varying laser fluence over a duration of at least one of the laser pulses. The spatiotemporally…

LLNL researchers have developed a Li-Sn-Zn ternary alloy and its method of production.  Instead of traditional alloying techniques, the alloy was synthesized using mechanical alloying (high energy ball milling).  With high purity elemental powders of lithium, tin and zinc, LLNL researchers were able to prepare Li60Sn20Zn20 as well as Li70Sn20Zn10 nanopowders.

CMI—a DOE Energy Innovation Hub—is a public/private partnership led by the Ames Laboratory that brings together the best and brightest research minds from universities, national laboratories (including LLNL), and the private sector to find innovative technology solutions to make better use of materials critical to the success of clean energy technologies as well as develop resilient and secure…

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 system consists of one or more flashlamp-pumped Nd:glass zig-zag amplifiers, a very low threshold stimulated-Brillouin-scattering (SBS) phase conjugator system, and a free-running single frequency Nd:YLF master oscillator.

The new LLNL technique works by transiently removing and trapping concrete or rock surface material, so that contaminants are confined in a manner that is easy to isolate and remove. Our studies suggest that 10 m² of surface could be processed per hour. The technique easily scales to more surface/hr.