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-…
Keywords
- Show all (77)
- Synthesis and Processing (16)
- Electric Grid (8)
- Materials for Energy Products (7)
- Additive Manufacturing (6)
- Carbon Utilization (6)
- Cybersecurity (4)
- Material Design (4)
- Computing (2)
- Direct Air Capture (2)
- Imaging Systems (2)
- Membranes (2)
- Power Electronics (2)
- Simulation (2)
- Additively Manufactured (AM) Optics (1)
- Geologic Storage (1)
- Inertial Fusion Energy (IFE) (1)
- Magnet Compositions (1)
- Material Characterization (1)
- (-) Data Science (5)
- (-) 3D Printing (4)
![Picture of SLA printed structures using 3D printable nitrile-containing photopolymer resins](/sites/default/files/styles/scale_exact_400x400_/public/2024-04/SLA%20printed%20structures%20using%203D%20printable%20nitrile-containing%20photopolymer%20resins.jpg?itok=cVxxoNNY)
![Picture of interlocked electrode structure with metal plated surfaces](/sites/default/files/styles/scale_exact_400x400_/public/2024-03/Interlocked%20electrode%20structure%20with%20metal%20plated%20surfaces.jpg?itok=1A_mLJdP)
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.
![Printed TPMS membrane structures using nanoporous photoresist](/sites/default/files/styles/scale_exact_400x400_/public/2023-12/Printed%20TPMS%20membrane%20structures.png?itok=siH1EwC9)
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-…
![Electrodeposition of Zn onto 3D printed copper nanowire (CuNW)](/sites/default/files/styles/scale_exact_400x400_/public/2023-07/ElectrodepositionofZnon3dprintedCuNW.jpg?itok=2G2D1kt9)
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…
![AI Innovation Incubator](/sites/default/files/styles/scale_exact_400x400_/public/2022-01/AI%20Innovation%20Incubator.jpg?itok=B8jcKPOy)
Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.
![Catalyst HPC cluster](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Catalyst%20HPC%20cluster.jpg?itok=k9uYS5xL)
Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…
![medical_x-rays_x-ray_tech](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/medical_x-rays_x-ray_tech_1.jpg?itok=kn0J-DkH)
Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…
![MimicGAN data set example](/sites/default/files/styles/scale_exact_400x400_/public/2020-05/mimicgan_robustness_to_rotation.png?itok=yacM18ra)
MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…
![medical_x-rays_x-ray_tech](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/medical_x-rays_x-ray_tech_1.jpg?itok=kn0J-DkH)
LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…