The humble battery: a device most people rarely think about until the phone runs low, the car won’t start, or the smoke detector beeps in the middle of the night.
While they may take a back seat in our interactions with the devices they power, batteries are a critical factor in advancing technological innovation.
It is the year 2035. In a world facing climate catastrophe, the human enterprise is powered by fields of wind farms, with turbine blades made from fast-growing grasses and the roots of a million-year-old fungus.
With a new grant from the U.S. Department of Energy's Solar Energy Technologies Office, Narayanan and his team of collaborators will develop 3D-printed high-temperature, high-pressure receivers for solar-thermal energy that can be used to generate power or for renewable industrial process heat.
Can we optimize how we cool our buildings without compromising campus comfort? This question, the focus of a long-standing partnership between UC Davis Facilities Management and UC Davis Chemical Engineering (Process Systems Engineering), has resulted in savings, greener energy use and published research.
Renewable energy is great for the Earth, but can be an intermittent source of power. We need electricity even when it’s dark and when the wind’s not blowing, and energy storage remains difficult. Engineers at UC Davis have risen to that challenge and prototyped a phase-change latent heat battery to store and release solar or wind energy as electricity whenever needed.
UC Davis and RePurpose Energy, a clean energy startup, have executed a licensing agreement for an innovative system that repurposes batteries from electric cars to use as energy storage systems with various applications, like solar power. The license provides RePurpose access to commercialize the technology developed at UC Davis.
Materials science and engineering associate professor Marina Leite thinks machine learning is key to the next big breakthrough in renewable energy. With a new three-year grant from the National Science Foundation, Leite will use machine learning techniques to study perovskite solar cells, a class of highly efficient but volatile devices, to find the optimal conditions to run them reliably.
When you think of solar power one of the first images that comes to your mind is most likely a bright sun shining down on a group of black cells on your neighbors’ rooftops. You think of the power of the sun saving you money during the day, but probably not saving you much once it sets. What if there was a way to run your house on solar power all day long, even at night? Electrical and computer engineering professor, Jeremy Munday, and his team are working to develop photovoltaic cells that can do just that.
More than a decade ago, Ruihong Zhang, a professor of biological and agricultural engineering at the University of California, Davis, started working on a problem: How to turn as much organic waste as possible into as much renewable energy as possible.