LLNL researchers have developed a method to enhance the performance of polyelectrolyte membranes by using a humidity-controlled crosslinking process which can be applied to precisely adjust the water channels of the membrane.

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…

LLNL researchers have developed a new 3D printable lithium-air battery that uses a novel thin solid state ceramic electrolyte.   LLNL’s invention overcomes the combined challenges of low power density and low cycle life in previously designed lithium-air batteries by using solid state electrolytes to achieve stability and multiscale structuring of the electrolyte to achieve low…

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 researchers have developed two novel TiCl4 based non-alkoxide sol-gel approaches for the synthesis of SiO2/TiO2 nanocomposite aerogels. Composite SiO2-TiO2 aerogels were obtained by epoxide-assisted gelation (EAG route) of TiCl4/DMF solution in the presence SiO2 aerogel particles. Additionally, the same TiCl4/DMF solution was employed to prepare SiO2@TiO2 aerogels by a facile one-…

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…

Nanomaterials that are emerging out of cutting edge nanotechnology research are a key component for an energy revolution. Carbon-based nanomaterials are ushering in the "new carbon age" with carbon nanotubes, nanoporous carbons, and graphene nanosheets that will prove necessary to provide sustainable energy applications that lessen our dependence on fossil fuels.

Carbon aerogels (CAs)…

Chemical and biological sensors based on nanowire or nanotube technologies exhibit observable ultrasensitive detection limits due to their unusually large surface-to-volume architecture. This suggests that nanosensors can provide a distinct advantage over conventional designs. This advantage is further enhanced when the nanosensor can harvest its meager power requirements from the surrounding…

LLNL has developed novel nanoporous carbon materials for the surface-stress-induced actuator technology. The morphology of these materials has been designed to combine high surface area and mechanical strength. The process allows for the fabrication of large monolithic pieces with low densities and high structural integrity. One actuation technology relies on electrochemically- induced changes…