Advancing early detection of biological events by digital holographic microscopy and simulation of microorganisms

There is a global need to advance bio-aerosol sensing for CBRN (Chemical, Biological, Radiological, and Nuclear) applications by compact and cost-effective devices. Employing digital holographic microscopy (DHM) and deep learning, we developed a system called HoloZcan to automate the analysis of airborne microbial pathogens and particles. DHM provides valuable information, but obtaining data from biological specimens for robust investigations is challenging. This paper introduces a custom simulation approach using the open-source software Meep and the finite-difference time-domain (FDTD) method to overcome limitations of existing Mie-based simulators, especially when dealing with complex microbial shapes. The simulation tool enables the modelling of specific microorganisms, offering a safer and more flexible alternative for CBRN research by bypassing ethical and logistical constraints associated with live pathogens. The study details the simulation workflow, built upon the construction of a database of optical properties of biological materials, for realistic simulations of light-microbe interactions. Evaluations on homogeneous and non-homogeneous objects demonstrate the tool's limited intrinsic errors and superior sensitivity to refractive index changes compared to traditional Mie-based simulations. This work significantly advances our capability to accurately simulate and analyse CBRN-related scenarios, enhancing comprehensive research in bio-aerosol sensing.