Collecting data on solar cell cracking and stress will prevent power loss and failure in solar cells.
Excess heat can be used as a clean, alternative form of energy for power plants and car engines.
Developing a way to use the temperature difference inside and outside of a building to generate energy could reduce energy demands.
Examining the Polytechnic campus’s energy infrastructure will allow ASU to understand how it can implement more renewable energy sources.
Creating a coating to allow single-paned windows to act as efficient, expensive double-paned windows will save energy and increase buildings’ comfort levels at lower cost.
Studying deep learning algorithms can lead to better solar energy prediction and management.
Power-efficient real-time illumination estimation will improve the appearance of mobile virtual reality worlds.
Understanding the physical properties of thin films is the key to implementing them in useful devices.
Developing a method to measure heat transfer will aid in understanding thermal energy harvesting and waste recovery.
Effective utilization of machine learning could accelerate the discovery of new energy-related materials.