Powder atomic layer deposition process is used to coat nanopowders of host materials (e.g. yttrium aluminum garnet) with optically active neodymium organometal precursor followed by O2/O3 RF plasma to convert to a single layer of Nd2O3. The process can be repeated to build arbitrarily thick layers with custom doping profiles and followed by post-…
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This invention proposes using a pulse laser configured to generate laser pulses and a controller for controlling operation of the pulse laser. The controller is further configured to control the pulse laser to cause the pulse laser to generate at least one of the laser pulses with a spatiotemporally varying laser fluence over a duration of at least one of the laser pulses. The spatiotemporally…
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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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Livermore Lab researchers have developed two new methods for improving the efficiency of laser drilling. The first method is based on multi-pulse laser technology. Two synchronized free-running laser pulses from a tandem-head Nd:YAG laser and a gated CW laser are capable of drilling through 1/8-in-thick stainless-steel targets at a standoff distance of 1 m without gas-assist. The combination of a…
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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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The new LLNL technique works by transiently removing and trapping concrete or rock surface material, so that contaminants are confined in a manner that is easy to isolate and remove. Our studies suggest that 10 m2 of surface could be processed per hour. The technique easily scales to more surface/hr.