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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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The novel LLNL technique uses electric fields to drive and control assembly. In the literature such methods have heretofore only formed disordered ensembles. This innovative method increases local nanocrystal concentration, initiating nucleation and growth into ordered superlattices. Nanocrystals remain solvated and mobile throughout the process, allowing fast fabrication of ordered…
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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…