2021 TomKat Graduate Fellowships for Translational Research Awarded
September 7, 2021
The hazards of climate change are growing and impacting our ecosystems, economies, and daily lives. TomKat Graduate Fellowships for Translational Research foster research and innovations coming out of Stanford labs so that they can be deployed to address sustainability challenges. In 2021, four advanced Ph.D. students have been awarded fellowships to advance their research toward meaningful applications.
Through these fellowships, the TomKat Center hopes to empower graduate students to perform the R&D needed to extend their research to impactful solutions. The students continue to benefit from the extensive Stanford ecosystem of experts and resources, allowing them to pursue their degrees while determining a viable outlet for their research and building a foundation for their future endeavors. The TomKat fellowships provide research funding, a stipend, and tuition support for up to two years.
“We were very happy to see another outstanding pool of applications for the Graduate Fellowships representing a broad spectrum of energy and sustainability research on campus” said TomKat Center Director Matt Kanan. “This year’s awardees are tackling problems that are important for energy storage and energy efficiency, resource utilization, and ecosystem protection. We’re excited about their potential to create solutions and advance new technologies.”
Ph.D. Student in Computer Science
Research Advisors: Prof. Moises Exposito-Alonso, Department of Plant Biology at the Carnegie Institution for Science and Biology, and Prof. Noah Goodman, Stanford CS and Psychology
Monitoring the Sustainability and Resilience of California’s Ecosystems From Space
This research focuses on fine-scaled quantification of Earth’s most ancient, widespread, and sustainable energy source: its natural ecosystems. The objective of the project is to map the photosynthetic resilience of California’s terrestrial ecosystems at meter-level resolution directly from satellite imagery utilizing deep neural networks. Such maps will enable fine-grained quantification of the photosynthetic ecosystem services California’s ecosystems provide, while also detecting ecosystems vulnerable to climate change and the causal factors underlying ecosystem decline in California over the past decade.
The resulting maps will enable decision makers, policy analysts, and conservationists to make precision decisions on what areas to prioritize for conservation and how to harden ecosystems to future climate shifts in order to protect our most sustainable yet threatened resource from the effects of global climate change. Long–term, this work has the capability to be rapidly scaled to a continental, and potentially global scale through its use of inexpensive, publicly available satellite imagery and open source citizen science observation data.
Ph.D. Student in Geological Sciences
Research Advisor: Prof. Jef Caers, Geological Sciences
Optimal sequential decision-making for subsurface exploration
It is estimated that global development of renewable energy technology and infrastructure will increase demand for metals such as lithium, cobalt, graphite, nickel, and copper by approximately 450% over the next three decades. This research will study data science techniques, decision theory, and algorithms for sequential decision-making commonly applied in autonomous navigation to improve exploration and uncertainty reduction in the subsurface. The systematic framework reduces bias and illuminates exploration decisions or sensitivities that otherwise may not be obvious to decision-makers.
The framework developed through this research will lead to an efficiently sourced and sustainable supply of the critical materials necessary to transition to renewable energy. In the future, the application can extend to other subsurface resources and storage reservoirs due to similarities in geological controls, data types, and exploration methodologies.
Ph.D. Student in Chemical Engineering
Research Advisors: Prof. Stacey Bent, Chemical Engineering, and Prof. Yi Cui, Materials Science Engineering
Regulating the Electrodeposition of Lithium for Stable High Energy Density Batteries
For humanity to successfully transition from fossil fuels, the mismatch in the demand and supply of renewable energy must be addressed using reliable high energy storage systems. One promising energy storage system is the lithium metal battery (LMB) because of its increased energy density over today’s lithium-ion battery technology. However, the lifetime of LMBs is hindered by morphological instabilities experienced during the electrodeposition of lithium. To control the electrodeposition of lithium metal, this research uses a novel architecture in which thin films are situated between lithium and the current collector. The films are purposely designed to possess specific chemical and electrical properties using atomic layer deposition (ALD) and have been used to demonstrate record efficiencies and electrochemical stability.
LMBs have the potential to provide twice the energy density of lithium-ion batteries. Practically, this implies that we could double the driving range of electric vehicles on one full battery charge. This research will address the instabilities that previously reduced LMBs lifetime performance. Additionally, since this technique is not electrolyte-dependent, it opens the range of applications to safer and less expensive electrolytes.
Ph.D. Student in Mechanical Engineering
Research Advisor: Prof. Ken Goodson, Mechanical Engineering
Development of Synergistic Evaporative Cooling and Water Harvesting/Recycling Technologies for Energy Efficient and Sustainable Operation of Data Centers
The energy demands from the operation of data centers are accelerating, currently they exceed 75 billion kWh/year in the US. Moreover, sustainable and water-saving operation of data centers is needed. This research will couple optimized and synergistic (1) high heat flux evaporative cooling based on capillary-driven flow in microporous structures, and (2) water harvesting/recycling technology for increased energy-efficient and sustainable cooling of data centers. This technology aims to continuously harvest and recycle water using a metal-organic frameworks (MOFs) fluidized bed, eliminating the need for energy and water-hungry vapor-compression refrigeration cycle that uses on-site wet cooling towers.
The goal is to design a system that will harvest and recycle water using the MOF-based fluidized bed that uses the low-grade waste energy/heat from the microprocessors. In the future, there is potential for this same technology to be applied for an on-site electricity generation and other industrial settings. The impact of the proposed research could also be extended to enhance the energy efficiency of power electronics, harvesting fresh drinking water, and building cooling systems.