Developing a Python-based Program for an Automated Scanning Fluorescence Microscope Setup with Integrated Spectral Analysis Capabilities

• Quoc Tuan Huynh, Physics, Lawrence University

• Aqiq Ishraq, Department of Materials Science and Engineering, University of Delaware
• Chitraleema Chakraborty, Department of Materials Science and Engineering, University of Delaware

Two-dimensional (2D) materials have recently attracted significant attention due to their unique electronic and optical properties and their ability to house localized defects with unique quantum properties. To investigate the localized quantum behavior arising from defect-based quantum emitters, researchers employ fluorescence microscopy for spatial mapping of their spectral properties when studying these materials. This requires an automated scanning fluorescence microscopy setup with integrated spectral analysis capabilities. Currently, the setup is limited to mapping out the total intensity without any way to distinguish between the spectral properties including different emission wavelengths. In this project, we aim to develop a Python-based program that enhances the capabilities of a scanning setup by integrating spectral analysis functionalities which include spatially mapping intensity, energy, and linewidth, from the acquired data. The setup currently collects two columns of data in a single acquisition at each scan point: energy and intensity. The total intensity can be easily mapped and visualized to identify fluorescence, but for effective spectral analysis, we also need to store the other axis, energy, and extract relevant parameters. To achieve this, we plan to implement fitting functions, such as Gaussian, Lorentzian, and Voigt, to extract the necessary spectral information from each pixel’s acquired spectra. Users will have the option to choose the fitting function that best suits their data. We intend to optimize the fitting process to handle large datasets efficiently. This spatial mapping of coordinates with the data will enhance the overall data acquisition process. By incorporating fitting functions and providing user-friendly interaction, researchers will be empowered to extract valuable spectral information from quantum emitters effectively. Furthermore, the optimized code will streamline the analysis of large datasets, facilitating deeper insights into the optical responses of these materials.