Project Description

Air conditioning accounted for 11% of the total commercial electricity usage and 16% of residential electricity usage, equating to 113.6 million metric tons of carbon dioxide emission in 2019. On the other hand, nationwide there are 14 million households without access to air conditioning, especially the underprivileged communities. It can be life threatening during extreme heat waves. Radiative cooling is a passive cooling method that can provide cooling by reflecting the solar radiation and emitting infrared heat both to deep space without energy input, hence greatly saves energy, reduces the carbon emission, and cools down our planet. Recently, Prof. Xiulin Ruan and co-workers have invented the whitest radiative cooling paints that can cool surfaces below the ambient temperature under direct sunlight, earning a Guinness World Record title of the “World’s Whitest Paint”. In this project, the Gilbreth fellow will work in broad areas of advanced radiative cooling paint and coating technologies, or other relevant topics of mutual interest. The fellow will be co-mentored by Professors Xiulin Ruan and Guang Lin, with their combined expertise on radiative cooling and machine learning. Potential tasks include but are not limited to multiscale simulations and design of ultrawhite, colored, and/or dynamic radiative cooling paints or materials. Machine learning models can be developed to enable accelerated prediction and high-throughput screening of materials, structures, and designs. The designed materials can be fabricated, characterized, and tested for validation and demonstration. 

Start Date

Spring-Fall 2025 

Postdoc Qualifications

The candidate is expected to have earned (or will earn in the near future) a PhD degree in Mechanical Engineering, Materials Science and Engineering, Chemical Engineering, Physics, or other related fields. Relevant previous research experience and scientific publications are desired. The candidate should also have excellent writing and speaking skills. 

Co-advisors

Xiulin Ruan, Professor, School of Mechanical Engineering. Email: ruan@purdue.edu, website: https://engineering.purdue.edu/NANOENERGY/

Guang Lin, guanglin@purdue.edu, Professor, Department of Mathematics (primary appointment), Professor, Department of Statistics, Professor, School of Mechanical Engineering, https://www.math.purdue.edu/~lin491/

Bibliography

Xiangyu Li, Joseph Peoples, Peiyan Yao, and Xiulin Ruan, "Ultrawhite BaSO4 Paints and Films for Remarkable Daytime Subambient Radiative Cooling," ACS Appl. Mater. Interfaces, 13, 21733-21739 (2021).

Xiangyu Li, Joseph Peoples, Zhifeng Huang, Zixuan Zhao, Jun Qiu, and Xiulin Ruan, “Full Daytime Sub-Ambient Radiative Cooling in Commercial-like Paints with High Figure of Merit”, Cell Rep. Phys. Sci. 1, 100221 (2020).

Andrea Felicelli, Ioanna Katsamba, Fernando Barrios, Yun Zhang, Ziqi Guo, Joseph Peoples, George Chiu, and Xiulin Ruan, “Thin layer lightweight and ultrawhite hexagonal boron nitride nanoporous paints for daytime radiative cooling,” Cell Reports Physical Science 3, 101058 (2022).

Ziqi Guo, Zherui Han, Dudong Feng, Guang Lin, and Xiulin Ruan, "Sampling-accelerated pre-diction of phonon scattering rates for converged thermal conductivity and radiative properties," npj Computational Materials 10, 31 (2024).

Ziqi Guo, Prabudhya Roy Chowdhury, Zherui Han, Yixuan Sun, Dudong Feng, Guang Lin, and Xiulin Ruan, "Fast and Accurate Machine Learning Prediction of Phonon Scattering Rates and Lattice Thermal Conductivity," npj Computational Materials 95, 1 (2023).