Dust-mitigating surfaces typically consist of high-aspect-ratio structures that separate particles from resting on the bulk material, thereby limiting adhesion due to short-range van der Waals forces. These surfaces can find uses in solar-panel coatings and a variety of dust-resistant optics. The current method for quantifying surface contamination is optical microscopy, but this method is inadequate for observing particles at the submicrometer scale due to the diffraction limit. Furthermore, regardless of the microscopy technique, particle identification becomes problematic as the particle contaminates approach the same length scale of the surface structures. In this work, we demonstrate a method to identify micro-/nanoparticle contaminates on nanostructured surfaces using electron microscopy and image processing. This approach allows the characterization of particles that approach the length scale of the surface structures. Image processing, including spectrum filters and edge detection, is used to remove the periodic features of the surface nanostructure to omit them from the particle counting. The detection of these small particles using electron microscopy leads to an average of 5.62 particles/100 μm2 detected compared to 0.63 particles/100 μm2 detected for the traditional confocal optical detection method. Beyond dust-mitigation nanostructures, the demonstrated particle detection technique can find applications in nanobiology, the detection of ice nucleation on a structured surface, and semiconductor mask inspections.
Identification of dust particles on a periodic nanostructured substrate using scanning electron microscope imaging
Andrew Tunell, Lauren Micklow, Nichole Scott, Stephen Furst, Chih-Hao Chang; Identification of dust particles on a periodic nanostructured substrate using scanning electron microscope imaging. J. Vac. Sci. Technol. B 1 December 2023; 41 (6): 062804. https://doi.org/10.1116/6.0003043
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