The FlowVAM system eliminates the need to manually exchange print volume in Tomographic Volumetric Additive Manufacturing and incorporates in situ metrology, yielding a 200-fold improvement in part production rates. Image credit: Hazel Rose Galvan/LLNL
Lawrence Livermore National Laboratory (LLNL) scientists and engineers have earned six R&D 100 Awards, which recognize the top 100 inventions worldwide. LLNL was the lead laboratory on four of the winning inventions and contributed to two multi-lab invention wins.
Often called the "Oscars of innovation," the trade journal R&D World Magazine recently announced the 2026 award winners. The R&D 100 awards recognize new commercial products, technologies and materials that are available for sale or license for their technological significance.
With this year's results, LLNL has now collected 192 R&D 100 awards since 1978. Submitted through the Laboratory's Innovation and Partnerships Office (IPO), these awards recognize the impact that Livermore innovation, in collaboration with industry partners, can have on the U.S. economy as well as globally. The winners were showcased at the 64th R&D 100 black-tie awards gala on Aug. 19 in Scottsdale, Arizona.
"The Laboratory's continued track record for excellence in innovation - that meets both our mission needs and industry challenges - is a testament to the unique expertise and creativity our scientists and engineers bring to their work," said IPO Director Matthew Garrett. "This year's winners illustrate LLNL's breadth of impactful solutions supporting U.S. national security and economic competitiveness, along with the importance of industry partnerships and collaboration."
LLNL's four primary award-winning technologies address needs in additive manufacturing, laser optics, polymer screening and gamma-ray detection; detailed below. A fifth LLNL technology, The Alloy Optimization Software (TAOS), also received a nod as finalist.
The two multi-lab award wins were co-developed with several other Department of Energy national laboratories. The Pele Suite of Reacting Flow Codes, led by the National Laboratory of the Rockies, harnesses the world's most powerful exascale computers to simulate the complex physics of turbulent reacting flows with ultra-high fidelity and allows users to easily modify its open-source code to integrate it with artificial intelligence. The development team included Cody Balos, David Gardner, Landon Owen and Carol Woodward.
Persistent DynAMICS, led by Los Alamos National Laboratory, is an intelligent sensing architecture that safeguards high-value assets and adds a layer of decision making on top of existing sensing systems to deliver real-time activity tracking. The LLNL development team was led by co-principal investigators (PIs) Siddarth Manay and Bob Priest and includes Zariluz Alvarado, Jim Curry, Joshua Dunn, Will Hunt, Jayanth Jagalur, Goran Konjevod, Joseph Macam, Brenda Ng, Phan Nguyen, Christine Ocampo, Garth Pratt, Randy Sanchez, Milton Smith, Lance Bentley Tammero, Aaron Wegner and Dave Watson.
FlowVAM - High-Speed Tomographic Volumetric Additive Manufacturing in Flow
Additive manufacturing enables many industries to rapidly prototype and test high-resolution parts for precise applications, including medical, energy, electronics and more. Yet these technologies must operate reliably at scale to be adopted and fully realize their potential.
Tomographic Volumetric Additive Manufacturing (tVAM), developed by LLNL and the University of California (UC) Berkeley, has shown promise for rapid parts production. It projects patterned light to a photosensitive material - curing it as a smooth finished product in seconds and a single step, rather than layer-by-layer methods that take hours or days to build one part. However, tVAM needs to manually exchange the system's print volume after each print, increasing process time and labor intensiveness.
Introduced in 2026, FlowVAM elevates tVAM to scale and advances one of the most material versatile 3D-printing technologies further toward manufacturing relevance, improving part production rates by up to 200 times. FlowVAM's design enables continuous production of high-value acrylic, plastics, glass, silicone and soft hydrogen parts for sensors, biocompatible scaffolds, micro-optics, metamaterials and more. Unlike competing technologies, FlowVAM incorporates in situ metrology for immediate process control feedback, ensuring quality without losing time and risking parts damage.
The FlowVAM team was led by PI Martin De Beer and includes Maxim Shusteff, Michael Triplett, Erika Fong, Aftab Bhanvadia, Wonjin Choi, Hazel Galvan, Mike Boyle and Aditya Mohan. FlowVAM builds on the successfully licensed and co-developed technology from LLNL and Hayden Taylor's group at UC Berkeley.
Precision Resistant OpTics with Enhanced Capping Technology
LLNL's PROTECT (Precision Resistant OpTics with Enhanced Capping Technology) addresses a simple but costly problem: protecting high-power laser systems from microscopic contamination.
High-power laser systems underpin a multi-billion-dollar global market spanning semiconductor manufacturing, advanced industrial processing, defense and scientific research. Particles such as dust or residue on optical surfaces absorb laser energy, heat rapidly and trigger localized damage that can crack or destroy the underlying coating. A single defect can cause catastrophic failure, resulting in costly downtime, component replacement and lost productivity.
PROTECT prevents this failure mechanism by applying a bonded, transparent protective cap directly to the surface of optical coatings. This cap acts as a thermal and physical barrier, preventing heat generated by surface contaminants from reaching and damaging the underlying optic. With modest upfront cost, this system-level solution can yield substantial lifecycle savings by preventing failures that can result in thousands to millions of dollars in downtime, repairs and lost productivity.
The PROTECT team was led by PIs Nathan Ray and former LLNL researcher Hoang Nguyen and includes Brad Hickman, Candis Jackson and James Nissen; co-developed with Coastline Optics, Inc.
High-Throughput Materials Screening with Studying-Polymers-On a-Chip
LLNL's High-Throughput Materials Screening with Studying-Polymers-On a-Chip (SPOC) technology addresses a fundamental need in polymer and composite formulation and testing by accelerating throughput of materials screening. Traditionally, polymers' viscosity requires a researcher to mix, cast and test the materials by hand, a painstaking and inefficient process when evaluating many potentially suitable polymer combinations. This slow workflow can hinder the development of next-generation materials.
SPOC automates mixing of polymers at viscosities that previously required hand mixing, enabling rapid and efficient testing, characterization and down-selection of materials. This platform mixes different material compositions in-machine and generates dozens of sample materials at a time onto one circuit board, which can then be immediately probed and tested by the same machine.
By streamlining formulation and analysis, SPOC reduces material optimization timelines from years to weeks. Researchers can then focus on meeting the material need at hand and analyzing data. This frees up time for exploratory research in smart materials, metamaterials and beyond, accelerating and encouraging discovery in novel areas and identifying new markets for immediate industry and commercial impact.
In 2025, the California Energy Commission awarded startup DarmokTech and LLNL a $2 million grant over three years to pursue recyclable, sodium polymer-based batteries using the Laboratory's SPOC technology.
The SPOC team was led by PI Johanna Schwartz and includes Brian Au, Peter Carlson, Aldair Gongora, Buddhinie Jayathilake, Adam Jaycox, Jayvic Jimenez, Sichi Li, Michell Marufu, Travis Massey, Matt McBride, Tuan Anh Pham, Arianna Reay, Johanna Vandenbrande, Jianchao Ye, Marissa Wood, Xiaoting Zhong and more.
RadSim: An Open-Source Modular Gamma-Ray Detector Simulator
RadSim is an open-source, modular radiation detector simulation framework that rapidly generates synthetic gamma-ray measurement data for realistic detection scenarios. It integrates nuclear source emission generation, radiation transport through shielding and surrounding environments and detector response modeling into a single workflow, producing spectra representative of actual instrument measurements.
Before RadSim, researchers and security professionals faced a critical gap. Namely, generating realistic, scenario-specific gamma-ray measurement data was slow, fragmented and dependent on costly physical testing or disconnected tools. No single framework could simultaneously model how a nuclear source emits radiation, how it travels through real-world shielding and environments and how a detector would actually respond.
By making high-fidelity radiation simulation open and modular, RadSim enables users to train algorithms, test equipment and prepare for threats involving illicit nuclear materials, radiological emergencies and public space safeguarding. This faster, more flexible approach ultimately helps build a safer and better-prepared world.
The RadSim team was led by PI Dhanush Hangal and includes Bonnie Canion, Vincent Cheung, Simon Labov, Brandon Lahmann, Caleb Mattoon, Noah McFerran and Karl Nelson.
