The Laboratory in the News

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Experiment Improves Predictions of Uranium Dispersion

Researchers from Lawrence Livermore and the University of Illinois at Urbana–Champaign have demonstrated that the behavior of uranium after a nuclear incident is incompletely predicted by computational fallout models, which approximate the physical and chemical processes occurring when the nuclear fireball condenses. In particular, uranium oxide is assumed to condense in its most stable form after cooling below its boiling temperature. However, the study, published in the April 1, 2020, edition of Analytical Chemistry, finds that kinetically driven processes in a system of rapidly decreasing temperature can result in substantial deviations from chemical equilibrium.

Funded by the National Nuclear Security Administration’s Office of Defense Nuclear Nonproliferation Research and the Defense Threat Reduction Agency, the team synthesized uranium-oxide nanoparticles using a plasma flow reactor under controlled conditions of temperature, pressure, and oxygen concentration. The team also developed a laser-based diagnostic to detect uranium-oxide particles as they formed inside the flow reactor. Using this approach, the researchers gathered direct experimental evidence for a change in the molecular composition of uranium-oxide condensates as a function of oxygen concentration.

Livermore nuclear scientist and principal investigator Kim Knight, says, “This work provides the first, detailed experimental insights that help explain the longstanding problem of why uranium can exhibit variations in volatile behavior during nuclear fireball condensation.” Livermore’s Batikan Koroglu, lead author of the research paper, adds, “This study will improve our ability to predict uranium’s multiphase transport in nuclear incident scenarios.”
Contact: Batikan Koroglu (925) 422-1867 (koroglu1 [at] llnl.gov (koroglu1[at]llnl[dot]gov)).

Identifying COVID-19 Antibody Sequences

Using the Laboratory’s advanced supercomputing resources and a machine-learning computational platform, researchers have computationally designed antibodies targeting SARS-CoV-2, which have been successfully synthesized and have shown promising activity in in-vitro experiments. The initial research results appeared online in the April 10, 2020, edition of BioRxiv.

In just 22 days, using the SARS-CoV-2 protein sequence and known antibody structures for SARS-CoV-1 (a similar coronavirus that causes Severe Acute Respiratory Syndrome), the Laboratory team, led by data scientists Dan Faissol and Thomas Desautels, used a computational platform combining machine learning, bioinformatics, experimental data, structural biology, and molecular simulations to drastically narrow down the possible antibody designs predicted to target SARS-CoV-2. The Laboratory’s Corona and Catalyst supercomputers performed nearly 180,000 free-energy calculations of candidate antibodies with the SARS-CoV-2 Receptor Binding Domain.

The team’s first designs were subsequently synthesized and evaluated, with one showing promising activity. A second iteration of computational designs has now yielded additional and improved molecules. Several of these have demonstrated binding activity in in-vitro SARS-CoV-2 assays, including one that has also shown neutralization activity. This design-first approach to antibody discovery could lead to a fully computational and rapid design of targeted antibody therapeutics for pandemic response. For more on the Laboratory’s COVID-19 research, visit llnl.gov/coronavirus.
Contact: Dan Faissol (925) 423-2544 (faissol1 [at] llnl.gov (faissol1[at]llnl[dot]gov)).

Second Skin Protects against Multiple Agents

A multi-institutional team led by Lawrence Livermore scientist Francesco Fornasiero has developed a smart, breathable fabric designed to protect the wearer against biological and chemical warfare agents. In addition to protecting military personnel, this material could also be useful in clinical and medical settings. The work was published online in the April 27, 2020, edition of Advanced Functional Materials and represents the successful completion of a key milestone for a project funded by the Defense Threat Reduction Agency.

Personnel safety garments must contain materials that provide protective qualities to the wearer, but those same qualities also limit the materials’ breathability. Fornasiero says, “We made our smart material both breathable and protective by combining two key elements: a base membrane layer made from trillions of aligned carbon nanotube pores and a threat-responsive polymer layer grafted onto the membrane surface.”

As part of the research, the team demonstrated that the moisture vapor transport rate through carbon nanotubes (graphitic cylinders with diameters more than 5,000 times smaller than a human hair) is high and increases with decreasing tube diameter. In addition, these tubes are small enough to block biological threats. To add protection against chemical hazards, which are smaller in size than biological ones, a layer of polymer chains was grown on the membrane surface that reversibly collapses when in contact with the chemical threat, temporarily blocking the pores.

The enhanced properties of this material could improve the thermal comfort of the user and greatly extend the wear time of protective clothing, whether in a hospital or on the battlefield. In the next phase of the project, the team aims to incorporate on-demand protection against additional chemical threats and make the material stretchable for a better body fit, thus more closely mimicking the human skin.
Contact: Francesco Fornasiero (925) 422-0089 (fornasiero1 [at] llnl.gov (fornasiero1[at]llnl[dot]gov)).