Browsing by Author "Wang, Ziyang"

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    High-Dimensional Spectroscopy Analysis with Machine Learning Techniques
    (2023-04-04) Wang, Ziyang; Huang, Shengxi
    High-dimensional spectroscopy often provides rich information. Raman spectroscopy is a non-destructive molecular sensing method. However, Raman signals in bio-samples are hard to interpret, due to the high dimensionality. Measurement of optical spectroscopy is also complicated and requires high-end instrumentations and intricate data analysis techniques. Machine learning methods offer great opportunities to extract subtle and deep information in high-dimensional spectra. They can also assist measurement of complex optical spectroscopy of materials with simpler optical setups. In this work, we develop a platform that enables rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in animal brains. The method facilitates the study of AD and can be extended to other tissues, biofluids, and for various other diseases. We also propose a computational reflectometry approach based on a deep learning model called ReflectoNet. It predicts complex refractive indices of thin films on top of nontrivial substrates from reflectance spectra, which was not feasible previously.
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    Measuring complex refractive index through deep-learning-enabled optical reflectometry
    (IOP Publishing, 2023) Wang, Ziyang; Lin, Yuxuan Cosmi; Zhang, Kunyan; Wu, Wenjing; Huang, Shengxi
    Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet. Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.
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    Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications
    (MDPI, 2023) Ranasinghe, Jeewan C.; Wang, Ziyang; Huang, Shengxi
    Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.