Understanding the complexities of plasma to advance the development of fusion - a clean and abundant energy source - has been a focus of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) for more than 70 years.
Now, the Lab is applying its expertise in low-temperature plasmas to advance low-carbon-emission technologies for a sustainable and competitive U.S. manufacturing industry. Described by PPPL researchers as "electromanufacturing," this emerging field of research investigates ways to replace the energy provided by fossil fuels with clean electricity, including using plasmas in several industrial processes.
"The Lab has deep expertise in diagnosing and understanding plasmas to support the development of electrified sustainable manufacturing processes. By applying our expertise in diagnostics, control, and simulation and modeling, we can help to sustainably de-fossilize multiple industries."
- Emily A. Carter, Senior Strategic Adviser and Associate Laboratory Director for AMSS
Carter - who is also the Gerhard R. Andlinger Professor in Energy and the Environment; professor of mechanical and aerospace engineering, the Andlinger Center for Energy and the Environment; and applied and computational mathematics at Princeton University - oversees this new branch of work at the Laboratory, which is broadening the Lab's singular purpose into a multi-focus vision.
To this end, the Lab will engage in new partnerships and projects supported by the DOE to advance sustainability science basic research and bring discoveries to deployment.
Transforming domestic manufacturing using more energy‑efficient processes
Princeton NuEnergy Inc., a U.S.-based innovative clean-tech company spun out from Princeton University by two Princeton faculty members affiliated with PPPL and two postdoctoral fellows, recently received a $3.5 million grant from the DOE to advance energy-efficient manufacturing processes using plasmas. The Lab, along with Princeton University and Argonne National Laboratory, will partner with Princeton NuEnergy Inc. on this effort.
The funding comes from the DOE's Office of Energy Efficiency and Renewable Energy (EERE), which provided $61 million to 31 projects led by national laboratories, industry and academia to accelerate research, development and demonstration (RD&D) in domestic manufacturing. Projects selected will drive innovation to progress the next generation of materials and manufacturing and related energy technologies.
Using machine learning and artificial intelligence to advance sustainable processes
The DOE will also support new work by a team of researchers at PPPL, Princeton, and the University of California, Berkeley (UC-Berkeley) with 3 million in funding.
The project will use machine learning tools to optimize methane pyrolysis, a process that breaks down the powerful greenhouse gas into hydrogen gas and solid carbon, thereby producing CO2-free hydrogen and a durable carbon byproduct. The project will also use AI to investigate graphene functionalization, a sheet of carbon atoms arranged like a honeycomb, which have powerful applications in, for example, nanomaterials used in electronic devices and supercapacitors for energy storage. If the process can be optimized to make graphene, it can help accelerate commercialization by producing a valuable form of solid carbon.
The team includes PPPL's Igor Kaganovich and Yevgeny Raitses as co-principal investigators along with Alexander Khrabry of Princeton University. Ali Mesbah of UC-Berkeley is the principal investigator. Tasman Powis of PPPL supported the proposal development.
The funding comes from the DOE's Fusion Energy Sciences, which announced $29 million in funding for seven team awards for research in machine learning, artificial intelligence and data resources for fusion energy sciences. The teams include fusion and plasma researchers working in partnership with data and computational scientists through the establishment of multi-institutional, interdisciplinary collaborations.
Learn more about the Lab's expanded research focus.
