LLNL researchers have developed a method to enhance the performance of polyelectrolyte membranes by using a humidity-controlled crosslinking process which can be applied to precisely adjust the water channels of the membrane.
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![JFET Device Structure](/sites/default/files/styles/scale_exact_400x400_/public/2023-10/JFET%20device%20structure.png?itok=NRzXbnhk)
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…
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![graphic_of_simulation.png graphic_of_simulation](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/graphic_of_simulation.png?itok=eyhMWp8B)
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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![multichannel_pyrometer.jpg multichannel_pyrometer](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/multichannel_pyrometer.jpg?itok=x0sCe_BN)
LLNL researchers have designed and tested performance characteristics for a multichannel pyrometer that works in the NIR from 1200 to 2000 nm. A single datapoint without averaging can be acquired in 14 microseconds (sampling rate of 70,000/s). In conjunction with a diamond anvil cell, the system still works down to about 830K.
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![Machine Learning for Monitoring microfluidic microcapsules](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Machine%20Learning%20for%20Monitoring%20microfluidic%20microcapsules%20875_0.jpg?itok=cLdsZh03)
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…