May 11, 2020

Energy Research Seed Fund awards six grants to Duke faculty to kickstart innovative projects

Nicholas Institute for Environmental Policy Solutions

In 2020-2021, the Duke University Energy Initiative's Energy Research Seed Fund will support projects addressing renewable energy's integration into the grid, battery performance, electrochemical catalysts, utilities' decision-making, the energy-water nexus, and energy's connections with war and health.

The program will award six grants to projects involving thirteen faculty members from five Duke schools, investing a total of $249,590 in promising new energy research.

In this—the seventh annual round of funding—the Energy Initiative awarded five seed grants for new interdisciplinary projects and one stage-two grant to support the next phase of a previously funded project.

The first six rounds of funding from the Energy Research Seed Fund totaled $1,458,491. As of fall 2019, those rounds had generated more than three times their value in follow-on awards for Duke research.

"Even as we experience a period of social distancing, scholarly collaboration is still thriving at Duke, as these promising projects demonstrate," notes Dr. Brian Murray, director of the Energy Initiative.  "We look forward to the continued progress—and the potentially transformative outcomes—of these early-stage efforts to address our world's energy challenges."    

The 2020 round of awards is co-funded by the Energy Initiative, the Office of the Provost, Trinity College of Arts & Sciences, and the Pratt School of Engineering.

Funded Projects in 2020-21

Seed Grants

Generating Virtual Inertia Through Crowds of Photovoltaic Inverters
More electric power systems are relying on renewable energy coupled with power electronics, which has led to the retirement of synchronous generators. The resulting loss of inertia (energy stored in a rotating mass) threatens power systems' stability and renewables' continued growth. This project will develop a novel distributed control framework that operates on existing photovoltaic (PV) inverters to contribute virtual inertia and other grid-supportive functions. A 100% renewable, proof-of-concept microgrid will be used to demonstrate the framework's effectiveness.
Project Team:
Stefan Goetz: Psychiatry and Behavioral SciencesDuke University School of Medicine and Electrical and Computer EngineeringPratt School of Engineering [Member of the Duke Institute for Brain Sciences]
Miroslav Pajic: Electrical and Computer EngineeringPratt School of Engineering and Computer Science, Trinity College of Arts & Sciences
Jingyang Fang: Psychiatry and Behavioral Sciences, Duke University School of Medicine

Developing Computed Tomography-Based 3D Temperature Mapping for Battery Failure
Overheating of lithium-ion batteries can lead to premature degradation and catastrophic failure, including explosions. But current methods of measuring battery temperature are far from ideal. This project will develop a new method—X-ray thermal computed tomography— to more accurately map the temperature profile of lithium-ion batteries at the resolution of tens of microns.
Project Team:
Po-Chun Hsu: Mechanical Engineering and Materials Science, Pratt School of Engineering
Cristian Badea: Radiology, Duke University School of Medicine and Biomedical Engineering, Pratt School of Engineering

The Energy-Health Nexus in Wars in the Middle East
The destruction of civilian infrastructure, particularly energy infrastructure, is a prevalent feature of war-making in protracted conflicts in the Middle East and North Africa. This project will examine health impacts of the destruction of energy infrastructure as well as efforts to restore and rebuild it. The project will advance understanding of the connection between energy access and health outcomes and will identify best practices for energy infrastructure maintenance and reconstruction in countries affected by conflict.
Project Team:
Erika Weinthal: Nicholas School of the Environment and Sanford School of Public Policy [Affiliate of Duke Science and Society]
Jeannie Sowers: Political Science, University of New Hampshire

AI-Assisted Design and Synthesis of High-Entropy Materials
High-entropy materials offer unique, otherwise unattainable surface atomic structures that can help solve long-standing catalysis problems. This project will combine rapid synthesis and AI-based materials design to demonstrate the use of high-entropy materials as electrochemical catalysts, with the potential to impact a broad class of chemical reactions.
Project Team:
Jie Liu: Chemistry, Trinity School of Arts & Sciences
Stefano Curtarolo: Mechanical Engineering and Material Science, Pratt School of Engineering and Physics, Trinity College of Arts & Sciences

The Energy-Water Nexus in India
Electricity is crucial to economic development but expanding its use can lead to severe environmental issues. This is especially true in countries like India, where a primarily coal-based electrical system contributes to air pollution (from the emission of fly ash) and water contamination (from acid mine drainage). This project will address some of these key issues by evaluating the potential uses of natural gas found in shallow groundwater, of geothermal groundwater for heating, and of coal ash to neutralize and remove toxic metals from acid mine drainage. 
Project Team:
Avner Vengosh: Nicholas School of the Environment, Duke Global Health Institute, and Duke Kunshan University
Adrian Bejan: Mechanical Engineering and Materials Science, Pratt School of Engineering

Stage-Two Grants

Enabling Better Energy Decisions Through Better Interpretable Causal Inference
As power grids age, demand grows, and power becomes less reliable, power companies must adapt legacy grids and create new programs to cope with changing times. But those companies are currently struggling to determine what types of changes will produce their desired outcomes. This project builds on a previous seed grant to combine causal inference methods with ideas from machine learning to produce an approach for matching in causal inference that is substantially more accurate, interpretative, and scalable than any other method. 
Project Team:
Cynthia RudinComputer Science and MathematicsTrinity College of Arts & SciencesElectrical and Computer Engineering, Pratt School of Engineering
Sudeepa Roy: Computer ScienceTrinity College of Arts & Sciences
Alexander Volfovsky: Statistical Science, Trinity College of Arts & Sciences