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Q&A with James DeMuth, Seurat Technologies CEO and LLNL Technology Transfer Partner

Seurat Technologies was born from a problem CEO James DeMuth encountered while working at Lawrence Livermore National Laboratory’s National Ignition Facility. The solution is a new way to dramatically speed up large-scale metal 3D manufacturing while assuring high quality finished products.  

LLNL Startup, Seurat Technologies, Successfully Closed $21M Series B financing Round

Seurat Technologies, the 3D metal printing leader that is making manufacturing better for people and the planet, has closed a $21M Series B extension with investments from new investors Xerox Ventures and SIP Global Partners.

LLNL team develops real-time diagnostic for Liquid Metal Jetting 3D printing

Lawrence Livermore National Laboratory is developing a new diagnostic tool that can determine the quality of metal droplets and monitor liquid metal jetting prints in real time.

Advanced Manufacturing Technologies

LLNL has developed a system and method that accomplishes volumetric fabrication by applying computed tomography (CT) techniques in reverse, fabricating structures by exposing a photopolymer resin volume from multiple angles, updating the light field at each angle. The necessary light fields are spatially and/or temporally multiplexed, such that their summed energy dose in a target resin volume crosslinks the resin into a user-defined geometry. These light-fields may be static or dynamic and may be generated by a spatial light modulator (SLM) that controls either the phase or the amplitude of a light field (or both) to provide the necessary intensity distribution.

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 algorithm to interpret the status of the microfluidic process. Machine learning describes a class of algorithms in which a computer "learns by example" in order to derive meaning from (possibly) complicated or imprecise data. These algorithms can act as the "brain" that controls a robot/machine by "deciding" tasks based upon its perception of environmental cues.

LLNL is seeking industry partners to collaborate on quantum science and technology research and development in the following areas: quantum-coherent device physics, quantum materials, quantum–classical interfaces, computing and simulation, and sensing and detection.

A cold-spray chamber is shown during deposition, with the nozzle at the top of the image and a near-full density sample being fabricated in the center. Particles of the brittle thermoelectric bismuth telluride are accelerated to more than 900 meters per second, or almost Mach 3, in inert gas and directed onto a copper surface, laying down the strips that form the basis of a functioning thermoelectric generator to harvest waste heat. Graphic by Jacob Long/LLNL
Versatile Cold Spray (VCS) enables deposition of brittle materials, such as thermoelectrics, magnets, and insulators, while retaining their functional properties. Materials can be deposited on substrates or arbitrary shapes with no requirement to match compositions. The VCS system is low cost, easily portable, and easy to use. VCS has been developed in a collaboration between Lawrence Livermore National Laboratory and TTEC Thermoelectric Technologies (https://thermoelectrictechnologies.com/).
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 and an online optimizer for determining ideal process parameters throughout a given print. Computer vision and machine learning are used in the process to monitor and derive quantitative values describing filament quality, e.g. eccentricity, continuity of a filament, color as it relates to mixing quality, among other possible metrics.

LLNL pioneered the use of tomographic reconstruction to determine the power density of electron beams using profiles of the beam taken at a number of angles. LLNL’s earlier diagnostic consisted of a fixed number of radially oriented sensor slits and required the beam to be circled over them at a fixed known diameter to collect data. The new sensor design incorporates annular slits instead, and it removes limitations on the number of angles at which electron beam profiles can be taken. The new annular slit scanning method can profile a beam while only needing a span of only 180 deg. to acquire a full spectrum of data; this enables the sensor surface to be fabricated from a monolithic piece of refractory metal, which improves dimensional accuracy. The radial nature of the scan…


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.

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 dissolved or emulsified smaller domains. Particles can be spherical, oval-shaped or irregularly shaped with a size range of 1 μm - 10 mm.

LLNL has developed an optically clear iodine-doped resist that increases the mean atomic number of the part. AM parts fabricated with this resist appear radio-opaque due to an increase in the X-ray attenuation by a factor of 10 to 20 times. Optical clarity is required so that the photons can penetrate the liquid to initiate polymerization and radio opacity is required to enable 3D computed tomographic imaging for final inspection via X-rays. The refractive index of these resists is matched to that of the immersion medium of oil-immersion objective lenses. As a result, these resists may also be used with high numerical aperture immersion objectives during dip-in two-photon lithography – a submicron additive manufacturing technique for printing tall millimeter-scale structures.


The LLNL method for optimizing as built optical designs uses insights from perturbed optical system theory and reformulates perturbation of optical performance in terms of double Zernikes, which can be calculated analytically rather than by tracing thousands of rays. A new theory of compensation is enabled by the use of double Zernikes which allows the performance degradation of a perturbed and compensated optical system to be calculated with a matrix multiplication using paraxial quantities rather than by iteration involving tracing large sets of rays. Almost no additional ray-tracing beyond that used in nominal design is required.


LLNL has solved the challenges of depth-resolved parallel TPL by using a temporal focusing technique in addition to the spatial focusing technique used in serial writing systems. We temporally focus the beam (through optical set-up design) so that a sharp Z-plane can be resolved while projecting 2D “light sheets” that cause localized photo-polymerization. This enables printing of complex 3D structures in a parallel fashion. To minimize the errors arising from discretization of 3D structures, LLNL has also developed techniques to “bend” the 2D light sheet into a 3D surface for printing of curved features.

The inventive elements of the LLNL apparatus are the arrangement of the laser light, the digital mask, and the axis of the collimating optics and the relative size of the…


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 multiscale porosities. Arbitrary shapes can be printed according to the application requirements. Moreover, the structure of three levels of porosity can be tuned independently which enables application specific multiscale architectures. In this method, DIW is used to extrude a gelbased metals mixture from a small nozzle into 3D periodic porous structures. The "ink" materials used for DIW are…

k9 training aids
This technology uses AM printing methods applied to explosives materials. But unlike producing explosives parts, the explosive component is added at a low concentration of around 4 to 6 wt. %. This allows for the final form, to be labeled as a non-hazardous material. A suitable matrix (substrate) is selected that ultimately will be non-volatile (reducing improper training on contaminants) and meet the application of the user. The mixture is printed in to the shape needed cured if necessary (depending upon the matrix). If the target aid is an improvised explosive (mixture of an oxidizer and fuel), each component will be printed in separate layers with non-loaded layer in between. This will prevent mixing of the two components causing reaction. The aid is now ready for distribution to the…

LLNL scientists have developed a new metal additive manufacturing technique that uses diode lasers in conjunction with a programmable mask to generate 2D patterns of energy at the powder surface. The method can produce entire layers in a single laser shot, rather than producing layers spot by spot as is currently done in powder bed fusion methods.

Livermore materials scientists and engineers are designing and building new materials that will open up new spaces on many Ashby material selection charts, such as those for stiffness and density as well as thermal expansion and stiffness. This is being accomplished with unique design algorithms and research into the additive manufacturing techniques of projection microstereolithography, direct ink writing, and electrophoretic deposition.