LLNL’s invention is a photopolymerizable polymer resin that consists of one or more nitrile-functional based polymers. The resin is formulated for SLA based 3D printing allowing for the production of nitrile-containing polymer components that can then be thermally processed into a conductive, highly graphitic materials. The novelty of the invention lies in (1) the photo-curable nitrile-…
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LLNL researchers have developed a fabrication process for creating 3D random interdigitated architectures of anodes and cathodes, eliminating the need for a membrane to separate them. This approach is similar to the repeating interdigitated multi-electrode architectures that also were developed at LLNL.
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LLNL researchers have developed novel advanced manufactured biomimetic 3D-TPMS (triply periodic minimal surface) membrane architectures such as a 3D gyroid membrane. The membrane is printed using LLNL's nano-porous photoresist technology. LLNL’s 3D-TPMS membranes consist of two independent but interpenetrating macropore flow channel systems that are separated by a thin nano-porous wall. 3D-…
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Improving the active material of the Zn anode is critical to improving the practicality of Zn-MnO2 battery technology. LLNL researchers have developed a new category of 3D structured Zn anode using a direct-ink writing (DIW) printing process to create innovative hierarchical architectures. The DIW ink, which is a gel-based mixture composed of zinc metal powder and organic binders, is extruded…
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To get the best of both worlds – the sensitivity of LC-MS with the speed of PS-MS – and a functional substrate that can maintain sample integrity, LLNL researchers looked to 3D printing. They have patented a novel approach to create lattice spray substrates for direct ionization mass spectroscopy using 3D-printing processes.
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LLNL researchers, through careful control over the chemistry, network formation, and crosslink density of the ink formulations as well as introduction of selected additives, have been successful in preparing 3D printable silicone inks with tunable material properties. For DIW (direct in writing) applications, LLNL has a growing IP portfolio around 3D printable silicone feedstocks for diverse…
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LLNL’s method of 3D printing fiber-reinforced composites has two enabling features:
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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…