A new method for estimating nanoparticle deposition coverage from a set of weak-contrast SEM images.

Imaging nanomaterials in hybrid systems is critical to understanding the structure and functionality of these systems. However, current technologies such as scanning electron microscopy (SEM) may obtain high resolution/contrast images at the cost of damaging or contaminating the sample. For example, to prevent the charging of organic substrate/matrix, a very thin layer of metal is coated on the surface, which will permanently contaminate the sample and eliminate the possibility of reusing it for following processes. Conversely, examining the sample without any modifications, in pursuit of high-fidelity digital images of its unperturbed state, can come at the cost of low-quality images that are challenging to process. Here, a solution is proposed for the case where no brightness threshold is available to reliably judge whether a region is covered with nanomaterials. The method examines local brightness variability to detect nanomaterial deposits. Very good agreement with manually obtained values of the coverage is observed, and a strong case is made for the method's automatability. Although the developed methodology is showcased in the context of SEM images of Polydimethylsiloxane (PDMS) substrates on which silicone dioxide (SiO) nanoparticles are assembled, the underlying concepts may be extended to situations where straightforward brightness thresholding is not viable.