The platform has three major components:
(1) active-mixing direct-ink-write
(2) in situ characterization substrates or probes
(3) active learning experimental planning system.
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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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Dubbed the "LLNL Chemical Prism", the LLNL system has use wherever there is a need to separate components of a fluid. A few examples include:
- Chemical detection for known and previously unknown chemicals or substances
- Separation of biomolecules from a cellular extract
- Fractionation of a complex mixture of hydrocarbons
- Forensic analysis of…