LLNL researchers have devised a set of design principles that facilitates the development of practical TPMS-based two fluid flow reactors.; included in the design are these new concepts:
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Hierarchical Triply Periodic Minimal Surface Structures For Efficient Two Fluid Flow Reactor Designs
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![Filled (8,8) (left) and (15,15) (right) CNTs with [EMIM+][BF4- ] using SGTI with the proposed spliced soft-core potential (SSCP) approach](/sites/default/files/styles/scale_exact_400x400_/public/2023-10/Filled%20CNTs%20using%20SGTI.png?itok=Dy0ObN7i)
LLNL researchers have developed a novel simulation methodology using slow growth thermodynamic integration (SGTI) utilizing spliced soft-core interaction potential (SSCP). The approach to filling the molecular enclosures is a nonphysical one. Rather than filling the pores from the open ends this method creates steps in the algorithm that allow molecules to pass through the pore wall and…
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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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Livermore researchers have developed a method of fabricating functional polymer-based particles by crosslinking UV-curable polymer drops in mid-air and collecting crosslinked particles in a solid container, a liquid suspension, or an air flow. Particles could contain different phases in the form or layered structures that contain one to multiple cores, or structures that are blended with…
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By combining 3D printing and dealloying., researchers at LLNL have developed a method for fabricating metal foams with engineered hierarchical architectures consisting of pores at least 3 distinct length scales. LLNL’s method uses direct ink writing (DIW), a 3D printing technique for additive manufacturing to fabricate hierarchical nanoporous metal foams with deterministically controlled 3D…
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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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To overcome limitations with cellular silicone foams, LLNL innovators have developed a new 3D energy absorbing material with tailored/engineered bulk-scale properties. The energy absorbing material has 3D patterned architectures specially designed for specific energy absorbing properties. The combination of LLNL's capabilities in advanced modeling and simulation and the additive…