Longji Cui: Mechanical Engineering Promotion Seminar

Title

Tackling Thermal Energy and Sensing Challenges across 24 Orders of Magnitude from Picowatt to Terawatt with Ultrasensitive Probes and Machine Learning

Abstract

Heat is the most abundant and least exploited form of energy: roughly two-thirds of the primary energy consumed worldwide is rejected as waste heat, a terawatt-scale resource that current technologies convert poorly. At the opposite extreme, the physics governing how heat moves through nanometer-scale materials, interfaces, and junctions can only be resolved by measuring heat currents at the picowatt level.

This seminar will connect these two extremes, spanning 24 orders of magnitude, through three research thrusts developed in my group. First, I will present ultrasensitive calorimetric sensors and scanning thermal probes that resolve nanoscale heat flow in near-field radiative gaps, atomic-scale contacts, and low-dimensional materials, and their extension to quantum materials where the dissipation of emergent electronic states remains experimentally unsettled. Second, I will describe solid-state heat-to-power conversion using thermophotovoltaics, thermophotonics, and thermal rectennas, with emphasis on zero-vacuum-gap thermophotonics, which replaces the vacuum gap that has constrained near-field devices with a solid infrared-transparent spacer to unlock high-density thermal photons in a scalable architecture. Third, I will present an emerging ML/AI direction in multimodal sensing and information science that recovers hidden fine-scale information while avoiding non-physical hallucination.

Background

Dr. Cui's research group develops high precision instrumentation and computational techniques to explore energy transport, conversion, and dissipation at extreme scales. Our highly interdisciplinary research spans scanning thermal microscopy, picowatt resolution thermal sensors, atomic and molecular-scale electronics, and thermophotovoltaics.

This includes computational measurement transformation grounded in quantum information theory to enable super-resolution imaging, physics-guided calibrated thermography at million-pixel acquisition speed, and native-resolution RGB-T fusion to enable autonomous edge systems such as wildfire detection. Together, these efforts converge on a vision in which precision measurement at the quantum limit, scalable system integration, and frontier data-driven techniques reinforce one another to address pressing real-world power and energy problems from the nanoscale to the grid.