Image Analysis of Structured Surfaces for Quantitative Topographical Characterization

In the fields of functional materials, interfacial chemistry, and microscale devices, surface structuring provides an opportunity to engineer materials with unique tunable properties such as wettability, anti-fouling, crack propagation, and specific surface area. Often, the resulting properties are related to the feature sizes of the structured surfaces and therefore, it is necessary to accurately quantify these topographies. This work presents a step-by-step description of a method for the quantification of the size of periodic structures using 2D discrete Fourier Transform analysis coupled with data filtering techniques to optimize feature size extraction and reduce user bias and error. The method is validated using artificial images of periodic patterns as well as scanning electron microscopy images of gold films that are structured on different substrates. While image Fourier Transform has been used previously and is a built-in feature in some commercial and open-source image analysis software, this work details image pre-processing and feature extraction steps, and how to best apply them, which has not been described in detail elsewhere. This method can analyze engineered or natural periodic topographies (e.g., wrinkles) to enable the design of patterned materials for applications including photovoltaics, biosensors, tissue engineering, flexible electronics, and thin film metrology.