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.
Studying deep learning algorithms can lead to better solar energy prediction and management.
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.
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.
Effective utilization of machine learning could accelerate the discovery of new energy-related materials.
Developing a new low-cost, high-efficiency solar cell module configuration will transform the way power is generated on a terawatt scale.