Speaker: Dr. Shengxi Huang, Rice University 

Abstract: 2D materials offer enormous opportunities to build designer structures with widely tunable properties. The engineering of 2D materials presents unique opportunities for optoelectronic device and quantum information platform. When designing optoelectronic devices of 2D materials, spectroscopic permittivity of 2D material is a key parameter. While ellipsometry has been used to measure permittivity, it requires simple device structure, non-trivial parameter fitting, and special setup. This talk will first introduce a new machine-learning assisted approach to measure permittivity of 2D materials embedded in complex device structures without model fitting, which can facilitate a quick, accurate, and in-situ characterization of 2D and other thin film materials [1]. The second part of this talk focuses on the interaction of 2D materials with organic molecules and related sensing applications. In particular, a novel enhancement effect of molecular Raman signals on 2D surface was discovered, which offers a new paradigm of biochemical sensing with high specificity, high multiplexity, and low noise. The selection rule for the 2D material substrates has been revealed, which is critical for device design. Two sensing applications for Alzheimer’s disease [2] and respiratory viruses [3, 4]  will also be discussed.


[1] Z. Wang, et al. “Measuring complex refractive index through deep-learning-enabled optical reflectometry,” 2D Materials, 10, 025025 (2023).

[2] Z. Wang, et al. “Rapid Biomarker Screening of Alzheimer’s Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy,” ACS Nano, 16, 4, pp 6426–6436 (2022).

[3] J. Ye, et al. “Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning,” Proceedings of National Academy of Sciences, 119 (23) e2118836119 (2022).

[4] K. Zhang, et al. “Understanding the excitation wavelength dependence and thermal stability of SARS-CoV-2 receptor-binding domain using surface-enhanced Raman scattering and machine learning,” ACS Photonics. 9, 9, 2963–2972 (2022)



Shengxi Huang is an associate professor in the Department of Electrical and Computer Engineering at Rice University. She was an assistant professor at Pennsylvania State University in 2018-2022. Shengxi earned her PhD degree in Electrical Engineering and Computer Science at MIT in 2017. Following that, she did postdoctoral research at Stanford University. Shengxi is the recipient of multiple awards, including NSF CAREER Award, AFOSR Young Investigator Award, Johnson & Johnson STEM2D Scholar’s Award (6 awardees worldwide in 6 disciplines), Kavli Fellowship for Nanoscience, Jin Au Kong Award for Best PhD Thesis at MIT, and Ginzton Fellowship at Stanford University. Shengxi’s research interests involve light-matter interactions of quantum materials and nanostructures, as well as the development of new quantum optical platforms and biochemical sensing technologies.