LLNL researchers have developed a method which utilizes functional alcohols to depolymerize polyurethane crosslinked networks. The functional alcohols show 5X increase in the depolymerization efficiency compared with current state of art (e.g. methanol, ethylene glycol). The crosslinked polyurethane networks completely depolymerized into a liquid oligomer within 48 hours at ambient…
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LLNL researchers have developed a novel photocurable silicone useful for producing three-dimensional objects via additive manufacturing. The resin formulations consist of a mult-component siloxane polymers with different functionalities, a platinum catalyst, a photoinitiator, and other organic peroxides. The LLNL invention involves controlling the spatial and temporal aspects of the…
LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects. DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications.
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-…
The essence of this invention is a method that couples network architecture using neural implicit representations coupled with a novel parametric motion field to perform limited angle 4D-CT reconstruction of deforming scenes.
LLNL researchers have developed an innovative and uniform single-pot polymer multi-material system, based on a combination of 3 different reactive chemistries. By combining the three different constituent monomers, fine control of mechanical attributes, such as elastic modulus, can be achieved by adjusting the dosage of UV light throughout the additive manufacturing process. This…
LLNL’s novel technology automates the inspection process by using a scanning system that captures data within the walnut shell without having to open the shell. The system output gives a visual image inside the walnut shell sufficient to evaluate and rate the quality of the walnut. The system uses a camara and radar that can capture data at a rapid rate. This improves speed and…
The approach is to develop a solid-state X-ray imager based on the architecture of the Silicon Drift Detector (SDD) which uses a series of cathode strips on both sides of a silicon wafer to achieve bulk depletion and electron drift. The invention leverages this SDD functionality to achieve signal stretching of liberated charge carriers from X-Ray photons that converts the time domain…
To overcome challenges that existing techniques for creating 3DGs face, LLNL researchers have developed a method that uses a light-based 3D printing process to rapidly create 3DG lattices of essentially any desired structure with graphene strut microstructure having pore sizes on the order of 10 nm. This flexible technique enables printing 3D micro-architected graphene objects with complex,…
LLNL’s novel approach is to use a continuous moving camera with a scan speed of >1 mm/sec and a frame rate of 100 frames per second. The key is to have a light source that flashes with a duration of one nanosecond, thus essentially freezing the image with no blur. Clear images of high resolution can then be captured through a high-magnification objective lens (reflection mode)…
LLNL’s approach to the development of a wide-field, three-dimensional quantum (3DQ) microscope is to harness quantum entangled photons to form simultaneous 3D optical images, which could be a new paradigm for 3D volumetric imaging of biological specimens. The 3DQ microscope is comprised of a novel optical system with highly sensitive detectors and an on-demand light source of entangled…
To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…
LLNL's 3D X-ray imager combines two different hardware pieces. The first is an x-ray optic with a depth-of-field that is small compared to the object under investigation. Reflective Wolter type x-ray optics are one such design. These hollow optics have a relatively large collection efficiency and can be designed with a large field of view. The depth of focus, which is the distance over which a…
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.
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…
LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…