LLNL researchers have developed a self-supporting structural material that promises more efficient carbon capture specifically from air, but generally from all CO2 containing gas sources. The material is produced with a liquid high-amine-content precursor polymer that is functionalized by adding on polymerizable end groups.
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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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This invention solves a limitation in the current practice of adding hydroxyl functional groups to the aminopolymer through the use of an alternative synthetic approach. The novelty of our approach is to produce new structurally modified relatives of common aminopolymers (PEI and PPI) as well as new functionalized materials in which the hydroxyl groups are tethered to a carbon in the backbone…
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This invention describes a multiple nozzle microfluidic unit that allows simultaneous generation streams of multiple layered coaxial liquid jets. Liquids are pumped into the device at a combined flow rate from 100 mL/hr to 10 L/hr. Droplets are created with diameters in the range of 1 µm to 5 mm and can be created with 1-2 shell layers encapsulating fluid. Droplets created from the system can…
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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…