Lawrence Livermore National Laboratory

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Learning about the products of supernovae

When a supernova explodes, streams of its plasma flow together and through one another, producing filament-like structures that create their own magnetic and electric fields via the Weibel instability. These fields produce even more filaments, until they become so strong that the filaments stop flowing and a collisionless shock is produced. The concentration of particles produced by these explosions is very low; some particles might travel light-years without colliding. In a paper recently published by Physical Review Letters, a team of researchers including scientists from Lawrence Livermore National Laboratory details the first quantitative measurements of the magnetic field structure of plasma filamentation driven by the Weibel instability.

The powerful magnetic fields associated with the shock have another effect: their turbulent motion in the plasma accelerates charged particles to high energy, producing cosmic rays that can be observed on Earth.

Using the OMEGA facility at the University of Rochester’s Laboratory for Laser Energetics, the team heated pairs of 1-millimeter-diameter beryllium disks using 1-nanosecond laser pulses. The heated surfaces expanded, producing plasma flows with peak speeds of 3.3 million miles per hour. The researchers collided the flows and studied the behavior of the plasma at the collision center using the optical Thomson scattering diagnostic, which measures the temperature, density, and velocity of the plasma streams. This process enabled direct observation of the formation of plasma filaments due to the Weibel instability and measurement of the current and magnetic field associated with those filaments.
Contact: George Swadling (925) 423-8289 (

Fighting COVID-19 with machine learning

A team of Livermore materials and computer scientists are applying sophisticated machine-learning tools to the fight against COVID-19. Using state-of-the-art natural language processing, image analysis, computer vision, and visualization techniques, machine-learning tools are scanning nanomaterials literature to determine if information can be extracted and help accelerate COVID-19 research. The paper appears in the May 13 Journal for Chemical Information and Modeling

Nanomaterials are widely used at the Laboratory. Their shape, size, and composition can impart unique optical, electrical, mechanical, or catalytic properties needed for a particular application. However, synthesizing an individual nanomaterial and scaling up its production is often challenging because a small change in the process or the addition of a specific chemical can have a dramatic effect on the product. These effects have historically only been discovered by time-consuming trial-and-error experimentation and by reading the scientific literature.

The new machine-learning tools have enabled the creation of a personalized knowledge base for nanomaterials synthesis that can be mined to help inform further development. Starting with approximately 35,000 nanomaterials-related articles, the team developed models to classify articles according to the nanomaterial’s composition and morphology, extract synthesis protocols from within the article text, and extract, normalize, and categorize chemical terms within synthesis protocols.  

In addition to processing articles’ text, microscopy images of nanomaterials within the articles are automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, a complementary browser-based visualization tool was developed that provides flexibility in comparing subsets of articles of interest. 
Contact: Anna Hiszpanski (925) 422-8987 (

Laboratory team helps develop tularemia vaccine

Two Lawrence Livermore researchers who have worked to develop a tularemia vaccine are part of a three-institution team that has been funded to make their vaccine candidate ready for use.

The two biomedical scientists, Nick Fischer and Amy Rasley, have worked for eight years on this research and will collaborate with scientists from the University of New Mexico and the Tulane National Primate Research Center under a five-year, $7.5 million grant from the Defense Threat Reduction Agency. 

Using the candidate vaccine, the LLNL scientists have demonstrated the ability of a subunit vaccine, incorporating different antigens from the  Francisella tularensis bacteria into a single particle, to protect against high doses of this bacteria when aerosolized.  F. tularensis is the bacteria that causes the disease tularemia, more commonly known as rabbit fever. 

The scientists will build on a nanotechnology—called nanolipoprotein particles (NLPs)—that was developed at the Laboratory for delivering vaccines and drugs inside the human body. Using the NLPs as a delivery platform, the  F. tularensis antigens can be co-delivered with another molecule, which stimulates the immune response against the antigens. Lab researchers see NLPs as flexible tools that can broadly be applied to developing vaccines for different pathogens. 

F. tularensis is classified as a class-A, high-priority pathogen and select agent by the Centers for Disease Control and Prevention. It is considered a potential biothreat agent based on its extremely low infectious dose. Disease manifestations vary depending on the route of exposure. It is an infectious disease that can cause fever, skin ulcers, enlarged lymph nodes, pneumonia, and throat infection with inhalational disease (pulmonary tularemia) being most severe. 
Contact: Amy Rasley (925) 423-1284 (