Effective thermal management of electronic components is increasingly critical across a wide range of applications—from portable devices like smartphones and laptops to large-scale systems such as data centers and astronomical satelites. In our research, we focus on developing novel, passive cooling solutions to address these challenges. Specifically, we design and optimize oscillating heat pipes (OHPs), which offer exceptionally high effective thermal conductivity—surpassing that of any known solid material. Our work introduces a novel theoretical framework to analyze and predict OHP operation failure based on thermal and thermodynamic behavior. Furthermore, we propose a wettability-based novel design criterion that addresses a long-standing challenge in OHP optimization, paving the way for more reliable and efficient thermal management solutions
In addition to passive cooling, our work also explores state-of-the-art active thermal management using electrospray system. We have successfully developed a multiplexed electrospray system, significantly enhancing its operational flexibility. Notably, we have expanded the achievable flow rate and operating voltage range by factors of 12× and 5.4×, respectively. These advancements open new possibilities for scalable and adaptive cooling in high-performance electronic systems where precise thermal control is essential.
Controlling surface wettability is essential for optimizing material performance in diverse applications, including heat transfer, anti-corrosion protection, biomedical devices, and self-cleaning surfaces. By engineering surfaces to be either superhydrophobic or superhydrophilic, we can precisely control how liquids interact with them. Developing low-cost, durable coatings on surfaces ranging from planar to complex geometries significantly broadens their practical utility in both industrial and scientific domains.
In our work, we develop novel methods to fabricate highly durable superhydrophobic and superhydrophilic surfaces. These include depositing carbon shoot (CS) on slippery liquid-infused porous surfaces (SLIPS) with chemically engineered porosity, or employing co-sputtering techniques to tailor surface chemistry and achieve the desired wettability.
Machine learning is emerging as a powerful tool for predictive modeling and data-driven analysis in thermal sciences. In our work, we use it to uncover complex patterns in oscillating heat pipe (OHP) behavior, predict heat transfer performance under varying conditions, and analyze fluid flow images. By integrating physics-based understanding with data-driven models, we aim to gain deeper insight into underlying mechanisms and enhance the optimization of thermal management systems. Additionally, we also explore machine learning to guide surface wettability design by predicting outcomes based on co-sputtering chemical composition parameters