LLNL researchers have developed a technology suite that includes several methods for detecting trace levels of illicit drugs even in mixtures. These methods can be used as a rapid screening test for incoming samples; for the samples that were determined to contain detectable amounts, they would undergo final verification using conventional laboratory analytical techniques.
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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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Livermore researchers have developed a method of fabricating functional polymer-based particles by crosslinking UV-curable polymer drops in mid-air and collecting crosslinked particles in a solid container, a liquid suspension, or an air flow. Particles could contain different phases in the form or layered structures that contain one to multiple cores, or structures that are blended with…
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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…