Washington Clean Energy Testbeds announces 2025 Undergraduate Research Awards

Tristan Angeles, Dijia Bao, Andrea Guiley, and Gabrielle Zaher receive grants to perform novel clean energy research at the UW’s open-access labs for climate tech innovation 

March 24, 2025

The Washington Clean Energy Testbeds, an open-access climate technology lab operated by the University of Washington (UW) Clean Energy Institute (CEI), has granted four UW students Undergraduate Research Awards.

Established in 2023 thanks to a generous philanthropic gift to CEI’s Innovation Fund, the Testbeds Undergraduate Research Award provides UW students with $3,000 over three academic quarters to perform novel research at the Testbeds. All UW undergraduate students in their third academic year or higher are eligible to apply for support for research in clean energy, advanced manufacturing, and related fields.

“I’m pleased to support more opportunities for undergraduate research at the Testbeds,” said Testbeds Managing Director Dr. Michael B. Pomfret. “This program provides young scientists and engineers with invaluable experience working in the same labs as cutting-edge companies, while our expert staff scientists mentor them. We’re excited about these projects and the future endeavors of these innovative Huskies.”

The projects supported by 2024–25 Testbeds Undergraduate Research Awards are:

Utilizing generative AI models for synthetic battery datasets

Within the battery industry, machine learning (ML) and artificial intelligence (AI) are increasingly being used to predict battery behavior and performance during use. But without robust training data, the outputs of ML/AI models will be inaccurate and effectively useless in helping advance the battery industry. Furthermore, there is a lack of open-source battery data to properly train these models because testing a physical battery takes significant time and resources, often requiring hundreds of hours of testing.

The goal of this project is to develop a generative AI model that addresses the lack of data by generating synthetic battery cycling data that is robust and field-relevant. The concept is that the AI would be able to output a cycle-based dataset given a set of parameters, such as cell type, temperature, and expected operation. By performing physical cycling tests on batteries with these same parameters, the Sun Lab can cross-reference the model’s output data with the test data obtained from the battery cyclers. With a validated, open-source generative AI model, battery researchers could generate training data repositories for their own AI/ML models — theoretically replacing a multi-year, time- and resource-intensive endeavor with a GitHub package and a few clicks.

Stabilizing 2D vanadium carbide nanosheets with environmentally benign salts

MXenes are an emerging class of two-dimensional materials with excellent electronic conductivity and other interesting properties suitable for applications in energy storage, sensing, catalysis, and optics. A MXene of particular interest for devices is vanadium carbide (V₂C). To create V₂C nanosheets, researchers synthesize V₂C materials with many layers, then they insert ions to increase the space between the layers, making it easier to peel off individual sheets from the bulk V₂C. Conventionally, these “intercalated” ions are large, organic, alkaline molecules that are toxic and difficult for nature to decompose. These ions also cause V₂C degradation, which releases toxic vanadium compounds into surrounding environments and prevents the widespread use of this material in many applications.

This project involves replacing large organic intercalating ions with smaller alkali cations to enhance the stability of V₂C nanosheets. Cations such as potassium and sodium spontaneously insert themselves between the MXene layers and are highly stable in aqueous solutions, which strengthens the MXene surface and makes it more resistant to breaking which will lower the degradation rate of the MXene.

Developing nonlinear electrochemical impedance spectroscopy (NLEIS) for battery characterization

The mining of battery materials and the improper disposal of batteries can be hazardous to the environment. Extending battery life spans can reduce these hazards by both limiting the need for new batteries, and therefore battery materials, as well as reducing battery waste. While thorough methods of testing battery health are essential to this goal, methods for determining the state of health of a battery without knowing its cycling history leave a lot of room for improvement. For example, when an electric vehicle is supercharged, the higher current degrades its battery more quickly. Most batteries are cycled without data collection and storage, or else that data is hidden from the battery user.

This project aims to further develop the use of nonlinear electrochemical impedance spectroscopy (NLEIS) for non-destructive battery characterization. The project involves testing fresh pouch and coin cells, then analyzing aged cells to investigate cell degradation. The data will be used to enhance physics-based modeling that combines NLEIS and conventional EIS to differentiate between battery states of charge and states of health.

Anion exchange doping of polymer semiconductors

To improve the conductivity of a semiconducting polymer, researchers commonly use a technique called molecular doping: immersing a polymer film in a solution of molecules that diffuse into the film and change the overall electrical properties of the device. The resulting films tend to be relatively unstable in air and when exposed to light, but researchers have recently demonstrated more stable conductivity in polymer semiconductors using a new technique known as anion-exchange doping (AED). AED enables the use of air-stable ions as well as increased dopant levels compared to conventional molecular doping methods.

The goal of this project is to improve the power conversion efficiency (PCE) currently achievable in perovskite solar photovoltaic (PV) devices by using AED to modify the hole transport layer (HTL) — the part of a solar cell that is responsible for moving the positive charges that are left behind by electrons as they are energized by incoming light. Successfully boosting HTL conductivity will reduce resistance in perovskite PV devices, increasing their PCE. The result could be a revolutionary improvement in perovskite PV performance as the technology approaches industrial scale.

Learn more about the Testbeds Undergraduate Research Award here.