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LLNL energy grid protection device
Ultrafast Energy Grid Protection

The approach is to leverage the fact that a momentary “load” equal to the power transmission line impedance, (Z0), during the transient can suppress its propagation.  Z(0) is typically a fixed impedance of several hundred ohms based on the geometry of most single wire transmission lines.

So, an isolated self-powered opticondistor (OTV) system may provide an ultrafast method of…

JFET Device Structure
High Voltage and High Power Diamond JFET Switch with Improvements

LLNL’s novel approach is to use diamond substrates with the desired donor (nitrogen) and acceptor (boron) impurities.   In order to optically activate these deep impurities, the invention requires at least one externally or internally integrated light source.  The initial exposure to light can set up the desired conduction current, after which the light source could be turned off.  Even with…

Graphic of NMR
Equipment to enable operando magic angle spinning NMR spectroscopy

There are prominent technical challenges arising from spinning a battery on the order of kilohertz as required by magic angle spinning in order to obtain spectral resolution that are addressed and enable operando solid-state NMR. The operando NMR measurement allows for continuous monitoring of the battery components and of potential metastable states that may exist during…

graphic_of_simulation
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…