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3D MEA device prior to actuation. A) A completed device. B) Close-up image of a single cell culture well. The large dark metal features at the top and bottom of each cell culture well are ground electrodes, which are all electrically shorted to each other. C) Light micrograph of a single 3DMEA post-actuation. The hinge regions are plastically deformed and allow the probes to stand upright without additional supports.
A 3-dimensional in vitro flexible microelectrode array (MEA)

To replicate the physiology and functionality of tissues and organs, LLNL has developed an in vitro device that contains 3D MEAs made from flexible polymeric probes with multiple electrodes along the body of each probe. At the end of each probe body is a specially designed hinge that allows the probe to transition from lying flat to a more upright position when actuated and then…

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Machine Learning Informed Control Systems for Extrusion Printing Processes to provide Optimal Control for Additive Manufacturing
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…
Machine Learning for Monitoring microfluidic microcapsules
Machine Learning for Automated Monitoring and Control of Microfluidic Systems
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…