Many of the disadvantages of current interface devices can be overcome with LLNL’s novel interface design, which relies on area array distribution where independent interface connector subassemblies are positioned in a planar grid. Not only is the interface device expandable area-wise (without increasing contact force), but it could also be expanded height-wise, with multiple layers of these…
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Commercial fiber optic cables are the current standard for carrying optical signals in industries like communications or medical devices. However, the fibers are made of glass, which do not have favorable characteristics for applications that require flexibility and re-routing, e.g. typically brittle, limited selection of materials, dimension constraints.
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Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring…
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LLNL researchers have developed a system that relies on machine learning to monitor microfluidic devices. The system includes (at least) a microfluidic device, sensor(s), and a local network computer. The system could also include a camera that takes real-time images of channel(s) within an operating microfluidic device. A subset of these images can be used to train/teach a machine learning…
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LLNL has developed a brain-on-a-chip system with a removable cell-seeding funnel to simultaneously localize neurons from various brain regions in an anatomically relevant manner and over specific electrode regions of a MEA. LLNL’s novel, removable cell seeding funnel uses a combination of 3D printing and microfabrication that allows neurons from select brain regions to easily be seeded into…