OAK RIDGE, Tenn., Nov. 20, 2018—Researchers at the Department of Energy’s Oak Ridge National Laboratory have received six R&D 100 Awards in recognition of their significant advancements in science and technology. The honorees were recognized over the weekend at the 56th annual R&D 100 Conference, sponsored by R&D Magazine.
The awards, known as the “Oscars of Invention,” honor R&D pioneers and their revolutionary breakthroughs in materials science, biomedicine, consumer products and more from academia, industry and government-sponsored research agencies. This year’s six honors bring ORNL’s total of R&D 100 awards to 216 since their inception in 1963.
ORNL researchers were recognized for the following innovations:
High Voltage Electrolytes for Ultracapacitors, developed by a team of ORNL researchers.
Ultracapacitors are energy storage devices capable of storing and quickly discharging large bursts of power but are limited by a narrow voltage window and low energy density.
ORNL researchers have developed a sodium-based electrolyte for ultracapacitors that extends the voltage window without side reactions and can double the specific energy while maintaining power density.
The breakthrough electrolyte is matched with special-made electrodes made of high-surface-area carbons bound with a stable polymer that won’t corrode in the new electrolyte formulation. The resulting combination enables a higher voltage device that necessitates fewer cells in a supercapacitor module, lowering costs while increasing module capacitance. It is also less toxic and more environmentally benign than traditional electrolytes, making it safer for grid storage and other low-cost, high-power energy storage solutions.
The ORNL development team, led by Jagjit Nanda, included Frank Delnick, Rose Ruther and Landon Tyler.
This project was funded by ORNL Seed Money, ORNL’s Laboratory Directed Research and Development Program and ORNL’s Technology Innovation Program.
Multinode Evolutionary Neural Networks for Deep Learning (MENNDL), developed by a team of ORNL researchers.
The MENNDL deep-learning technology was designed to mimic human thought processes. When analyzing a dataset, the software starts with a poorly performing network, then alters it through a series of feedback loops to optimize its performance within hours.
MENNDL’s evolving networks are applicable in many fields from computer vision to speech recognition and can be trained to analyze specific datasets. The system presents a novel application of deep learning algorithms to electron microscopy, as it is able to extract structural data from raw atomic microscopy information.
MENNDL is also a potentially more efficient approach to data analysis, as its custom-generated neural networks can match or exceed the performance of handcrafted networks. The system has run on the Summit supercomputer, but future developments will allow MENNDL networks to run on smaller computers.
The ORNL development team was led by Robert Patton and included Thomas Karnowski, Seung-Hwan Lim, Thomas Potok, Derek Rose and Steven Young.
MENNDL was funded through ORNL’s Laboratory Directed Research and Development Program.
The Atomic Forge, developed by a team of ORNL researchers.
The Atomic Forge is a new fabrication approach that repurposes a scanning transmission electron microscope (STEM) to assemble and manipulate matter atom-by-atom. The technology uses STEM electron-beam modification, custom beam control and real-time feedback to create 3D nanometer-scale crystalline structures and controllable atomic assemblies in 2D and 3D, one atom and one atomic plane at a time.
The Atomic Forge is the first to use the STEM approach and the first to allow sample monitoring during manipulation. Fabrication time is significantly faster than scanning tunneling microscope (STM) assembly, from hours to minutes, and can be operated at room temperature with a high vacuum, rather than cryogenic temperatures and an ultrahigh vacuum. The controller-software module is compatible with modern STEMs and provides a pathway towards large-scale fabrication of materials with pre-defined properties, Beyond-Moore’s-Law technologies, quantum computing devices and the realization of complex molecular machines and nanorobotics.
The development team, led by Sergei Kalinin and Stephen Jesse, included Ondrej Dyck, Albina Borisevich, Bethany Hudak, Andrew Lupini, Raymond Unocic and Ivan Kravchenko.
Funding for this project was provided by ORNL Seed Money, ORNL’s Laboratory Directed Research and Development Program and DOE’s Office of Science.
The TNT Cloning System, developed by a team of ORNL researchers.
The TNT Cloning System allows for speedy multi-gene DNA synthesis and assembly from universal gene libraries. The system is all-inclusive, capable of handling large DNA constructs at speeds up to 80% faster than other assembly systems.
It adopts a pre-defined three-nucleotide signature—TNT—and a buffer system for quick one-pot reactions. Enzymes create specific signatures that allow three elements to be combined at once per round of assembly. This universally exchangeable system is transferable across hosts including bacteria, yeast, plant and mammalian cells.
This system is unique, as it does not require adaptors, sequence homology, amplification or fragment mutation to construct DNA. The technology allows for greater access among research communities and advances cheap and environmentally friendly bio-manufacturing efforts.
The development team, led by ORNL’s Gerald Tuskan, included Xiaohan Yang of ORNL and Henrique De Paoli, formerly of ORNL and the University of Tennessee, currently of Lawrence Berkeley National Laboratory.
The TNT Cloning System was funded by the ORNL Technology Innovation Program and the DOE Office of Science’s Genomic Science Program.
ORNL was also a partner on the following winners:
Ambient Reactive Extrusion Additive Manufacturing, submitted by PPG and co-developed with ORNL.
The Ambient Reactive Extrusion technology is a new approach to additive manufacturing that delivers faster, stronger and more versatile printed parts than traditional thermoplastic printing techniques. The ARE AM approach uses agile, lightweight print heads to deposit polymer materials up to 100 times faster than traditional print systems, suitable for on-site rapid prototyping. Carefully designed reactive print materials create inter-layer covalent bonds, resulting in significantly stronger vertical strength and eliminating internal stresses and warping.
The combination of technologies enables rapid vertical builds and complex geometries and does not require the same thermal management strategies as slower, heavier machines, enabling more cost-effective, energy-efficient additive manufacturing.
The ORNL portion of the development team was led by Orlando Rios and included William Carter, Lonnie Love, Peter Lloyd and Brian Post.
Mobile Universal Grid Analyzer (m-UGA), submitted by the University of Tennessee’s Power Information Technology Laboratory and co-developed by UT-ORNL Governor’s Chair Yilu Liu, with ORNL’s Thomas King and Lingwei Zhan..
The m-UGA is a time-synchronized, multi-functional, wide-area monitoring and analyzing device designed to enhance the situational awareness capabilities and assess power grid health in real time on mobile devices such as smartphones and tablets. The technology can be installed virtually anywhere with regular 120-V power outlets and is able to capture dynamic grid behaviors and monitor customer-end power quality.
ORNL is managed by UT-Battelle for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit http://energy.gov/science/. —Shelby Whitehead and Sean Simoneau