LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects. DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications.
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High throughput discovery of linear B-cell epitopes for the development of vaccines and therapeutics
LLNL’s high throughput method involves proteome-wide screening for linear B-cell epitopes using native proteomes isolated from a pathogen of interest and convalescent sera from immunized animals.
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LLNL has developed a method of extending device lifetimes by imprinting into the device a shape that excludes specific vibrational modes, otherwise known as a phononic bandgap. Eliminating these modes prevents one of the primary energy loss pathways in these devices. LLNL’s new method enhances the coherence of superconducting circuits by introducing a phononic bandgap around the system’s…
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The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…
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LLNL has developed a novel process of production, isolation, characterization, and functional re-constitution of membrane-associated proteins in a single step. In addition, LLNL has developed a colorimetric assay that indicates production, correct folding, and incorporation of bR into soluble nanolipoprotein particles (NLPs).
LLNL has developed an approach, for formation of NLP/…