Transparent conductive layers are a critical component in photovoltaic systems for the extraction of charge carriers from device active layers, but they create shadowing, optical absorption, and series resistance losses. Through computer modeling, I recently argued that performance and cost gains are achievable by deploying micron-scale grid and nanowire layers in place of traditional indium tin oxides, and I helped to justify an electrohydrodynamic inkjet (EHDIJ) printer purchase at the WCET. My future research will use machine learning techniques to optimize grid structures based on Murray’s Law scaling (found in biological transport systems) and to optimize EHDIJ recipes to achieve micron-scale line prints and prototype these advanced grids. I plan to demonstrate how machine learning, biological design principles, and additive manufacturing can combine to enable a leap in photovoltaic device performance.
Advisor: J. Devin MacKenzie – Materials Science & Engineering