Stanford Energy Student Lectures: Jiawei Zhou and Sara Ha

Jiawei Zhou

Title: Energy-efficient materials for the building envelope

Abstract: Energy efficiency is recognized as a key strategy to accelerate the transition to a clean energy society. In particular, the building sector contributes significantly to global greenhouse gas emissions, where space heating and cooling comprise a dominant fraction of the total energy consumption. Significant energy saving and emission reduction can be achieved if we can re-design the building thermal envelope. In this presentation, I will discuss materials engineering at the microstructural level for improving building energy efficiency. We will demonstrate an insulation structure that combines the insulation performance of vacuum insulation panels and the convenience of conventional foam insulations, i.e. can be cut, punctured or re-assembled. Such insulation materials can help to reduce the heating and cooling energy loads of the building and contribute to emission reduction.

Bio: Jiawei Zhou is currently a postdoc researcher working with Professor Yi Cui in the department of Materials Science and Engineering at Stanford University. He received his Ph.D degree in Mechanical Engineering at Massachusetts Institute of Technology in 2019. His work focuses on designing materials for improving energy efficiency broadly.

Sara Ha

Title: Electrochemical modeling framework for lithium metal batteries

Abstract: Lithium metal batteries (LMB) with metallic lithium anodes promise high energy densities up to 500 Wh/kg that nearly doubles the energy density of current lithium-ion batteries. In this work, we develop an electrochemical model based on the well-known Newman-Doyle-Fuller (DFN) model by replacing the graphite with a lithium metal anode. The solid electrolyte interface (SEI) is included in the model which affects the stripping/plating kinetics on the lithium metal surface. The LMB model requires over 20 parameters to characterize the geometric, electrochemical, and kinetic properties. The parameters are mathematically identified using particle swarm optimization (PSO) and experimental cycling data. This model presents the opportunity to predict the behavior of real-world LMB batteries under various applied current such as the cell voltage response and internal states such as lithium-ion concentration in the electrolyte.

Bio: Sara is a 4th year PhD Candidate in Mechanical Engineering supervised by Prof. Simona Onori in the Energy Science and Engineering Department at Stanford University. She received her M.S. in Mechanical Engineering at Stanford University in 2019 and her B.S. in Mechanical Engineering at Georgia Institute of Technology in 2016.