FURI | Fall 2024

Advancing Solar Energy Technology by Predicting Figure of Merit

Energy icon, disabled. An orange lightning bolt.

The goal of this study is to predict the Figure of Merit (ZT) values of different CoSb3 skutterudites based on parameters such as weight ratio and electronegativity. ZT values represent the efficiency of converting thermal energy to electrical energy. This study involves collecting ZT values from previous research articles, calculating weight ratios, and using computational simulations to predict ZT values for various CoSb3 molecules. Ultimately, this research aims to improve the performance of solar thermoelectric generators (STEGs) by discovering the best combinations of CoSb3 materials.

More projects from the current symposium

Po-Heng Ho

Electrical engineering

Optimizing Compact Model of Nanosheet FET (NSFET) for Analog/Digital IC Design

Developing an optimized machine-learning-aided compact model for nanosheet field-effect transistors can help carry out accurate and reliable circuit design.

Mentor: Kexin Li

Program: FURI

Tomoki J. Inoue

Materials science and engineering

Developing a Theoretical Understanding of Water-Network Formation On The Surface of Metal Oxides

This project explores the water-surface interface on metal-oxides through Density Functional Theory in conjunction with machine learning.

Mentor: Christopher Muhich

Program: FURI

Devesh Nath

Electrical engineering

Machine Learning on Embedded Systems to Assess Correct Form During Weight Training

This research harnesses machine learning to make weight training safer and more effective, potentially transforming fitness practices.

Mentor: Chao Wang

Program: FURI