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Advanced Optical Microscopy and Spectroscopy (AOMS) group

Peng Gao, Ke Lin, Jianlang Li, Lixin Liu, Ruiting Zhang, Zhiqiang Wang, Ying Ma, Lin Ma, Yan Lu, Min Liu, Juanjuan Zheng

The Advanced Optical Microscopy and Spectroscopy (AOMS) Team at the School of Physics and Optoelectronic Engineering (SPOE) is committed to studying advanced optical microscopy and spectroscopy technologies and applying them to biology and industrial inspection, etc. This team consists of 11 faculty members and 14 postgraduate students. The main research interests are listed as follows: super-resolution optical microscopy, quantitative phase microscopy, Raman spectroscopy, fluorescence correlation spectroscopy, fluorescence lifetime imaging. The group keeps collaborating with other high-level research institutions, including the State Key Laboratory of Transient Optics and Photonics at XIOPM, Air Force Medical University of PLA (the Fourth Military Medical University), Karlsruhe Institute of Technology (KIT), and the University Stuttgart in Germany for many years.

So far, the group has published more than one hundred papers in international academic journals such as Nature Photonics, Advance of Optics and Photonics, eLife, and Optics Letters. The publications have been cited over 1600 times. Some of the works have been selected as journal cover, research hotspot, and reported by oversea media overseas such as ScienceDaily, Physics News, etc. In addition, six invention patents have been authorized, prompting the industrialization of the investigated technologies.

Research topics: Being indispensable means to explore the micro-world, optical microscopy and spectroscopy not only plays an important role in fields of optics and optical engineering, but also promotes the development of other fields. Over the past two decades, especially with the development of chemistry science, optical and digital technology (e.g. spatial light modulator, digital micromirror array, electro-optic modulator, GPU parallel computing, and deep learning, etc.), optical microscopy and spectroscopy have been developed rapidly, and many new technologies emerge such as super-resolution imaging, light-field imaging, nonlinear optical imaging, computational imaging, fluorescence correlation spectroscopy, Raman spectroscopy, etc. These optical and spectroscopy approaches open a new gate for the unveiling of secrets in fields of biomedical, material chemistry, industrial production, etc. Researches on the new optical microscopy and spectroscopy both theoretically and experimentally has been one of the most active and innovative research fields.

1.    Super-resolution Optical Microscopy

Fluorescence microscopy has the advantages of minimally-invasive to live samples and visualization of specific chemical structures, and therefore it has been acting as the mainstream of the observation tools in biomedical research. However, due to the diffraction limit, the resolution of traditional fluorescence microscopy is limited to 200 nm, cannot meet the requirements for detailed information of samples. To break through the limit of optical diffraction and further improve the resolution of optical microscopy, herein we are investigating super-resolution imaging approaches such as structured illumination microscopy and stimulated emission depletion (STED) microscopy.

2.    Quantitative Phase Imaging

Phase distribution is an important characteristic of light-wave besides its intensity distribution. The phase distribution is directly related to the three-dimensional morphology or the thickness/refractive index distribution of the transparent object. Therefore, it is of great importance to explore quantitative phase imaging technology. In the AOMS group, digital holographic microscopy, single-beam phase microscopy based on modulated illumination are investigated and applied to 3D surface profile detection, live-cell observation, special beam generation, adaptive imaging, and so on.

3.    Fluorescence Lifetime Imaging

The lifetime of fluorescence can be changed with the variation of microenvironment around the fluorophore, but not affected by probe concentration, excitation light intensity, photobleaching effect etc., thus it can provide functional information such as metabolic state, pH, ion concentration etc. Fluorescence lifetime imaging microscopy (FLIM) offers high-precision three-dimensional imaging data, as well as accurate subcellular structure and dynamic imaging, which is widely used in biomedicine and surface analysis in material science.

4.    Fluorescence Correlation Spectroscopy

Fluorescence correlation spectroscopy (FCS) enables to quantifying the hydro-diameter and concentration of biomolecules (e.g. proteins) labeled with fluorescence. It is realized by measuring and analyzing the intensity fluctuation induced by the molecules diffusing through the light focus. Furthermore, when different molecules are labeled with different kinds of fluorescent dyes, the size, concentration, and interaction between the two molecules can be studied simultaneously. This technique can be applied to explore the regulatory effect and microcosmic mechanism of biological macromolecules (e.g. proteins) from the molecular level during the normal life process and disease.

 5.    Polarization-dependent Raman Spectroscopy

Polarization-dependent Raman Spectroscopy has the ability of analyzing the content and existence form of the target molecule through measuring the Raman scattering spectrum under the condition that the polarization of excitation laser is modulated. New analytical strategies such as Raman spectrum division, combined with the simulation based on theoretical quantum chemistry and molecular dynamics, can offer in-depth analysis of the target system and provide a profound understanding of the microstructure and dynamics of the molecular system, especially for drugs in aqueous solution and their interactions with macromolecules. In addition, Raman spectroscopy combined with deep learning analysis has also been widely used for quantitative quality analysis and safety examination of food, medicine and other products.

6.    Surface-Enhanced Spectroscopy

Surface-enhanced spectroscopy has the ability of significantly improving the intrinsic Raman/fluorescence/infrared spectrum intensity of matter based on surface plasmon polaritons (SPPs), which offers an efficient tool for chemical analysis. SPPs are able to trap light in scale of subwavelength or even a few nanometers through the interaction between the light and nanostructured metallic materials e.g. nanoparticles, nanorods, nanotapers, which can successfully break the diffraction limit and often accompanied by considerable electromagnetic field enhancement. Research on surface-enhanced spectroscopy helps to offer intensive nanoscale light source, and push forward the development of applied spectroscopy in cutting-edge technologies i.e. super-resolution imaging, single-molecule detection, optical sensing, etc.

7.    Fourier transform infrared Spectroscopy

Fourier transform infrared spectroscopy (FTIR) is able to analyze the content and characteristics of molecules in the target sample by measuring the absorption spectrum ranging from the near-infrared to mid-infrared. Both FTIR and Raman spectroscopy are vibration spectroscopy techniques, but FTIR is more sensitive to the vibration of molecules that have large dipole moment change. So FTIR is a favorable supplement to Raman spectroscopy. The technique can realize rapid and in-situ measurement and analysis for biological systems. Combined with new analysis strategies like spectral division and excess spectral division, as well as calculation methods like deep learning, FTIR can obtain a broadened application scope, especially in biomedicine and food safety examination etc.

8.    Deep Learning

Deep learning is one of the hot topics in the field of artificial intelligence. Through building an artificial Neural Network model with multi-hidden layer, and utilizing massive sample data to train the model, the multiple layers can progressively extract higher-level features from the raw input. Deep learning has the feature of learning hierarchical characteristics from data, and has been widely used in image recognition, data analysis, target detection, etc.