The edge of a reactive or topographical feature is hard to estimate from feedback-based scanning electrochemical microscopy due to diffusional blurring, but is crucial to determining the accurate size and shape of these features. In this work, numerical simulations are used to demonstrate that the inflection point in a 1D line scan corresponds well to the true feature edge. This approach is then applied in 2D using the Canny algorithm to experimental images of two model substrates and a biological sample. This approach circumvents the need for aligning the imaged region between separate microscopy techniques, reveals hidden details embedded in SECM images, and allows individual features to be separated from their background more effectively.

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The charge density of DNA is a key parameter in strand hybridization and for the interactions occurring between DNA and molecules in biological systems. Due to the intricate structure of DNA, visualization of the surface charge density of DNA nanostructures under physiological conditions was not previously possible. Here, we perform a simultaneous analysis of the topography and surface charge density of DNA nanostructures using atomic force microscopy and scanning ion conductance microscopy. The effect of in situ ion exchange using various alkali metal ions is tested with respect to the adsorption of DNA origami onto mica, and a quantitative study of surface charge density reveals ion exchange phenomena in mica as a key parameter in DNA adsorption. This is important for structure‐function studies of DNA nanostructures. The research provides an efficient approach to study surface charge density of DNA origami nanostructures and other biological molecules at a single molecule level.

Scanning electrochemical microscopy (SECM) has matured as a technique for studying local electrochemical processes. The feedback mode is most commonly used for extracting quantitative kinetic information. However, approaching individual regions of interest, as is commonly done, does not take full advantage of the spatial resolution that SECM has to offer. Moreover, fitting of experimental approach curves remains highly subjective due to the manner of estimating the tip-to-substrate distance. We address these issues using negative or positive feedback currents as a reference to calculate the tip-to-substrate distance directly for quantitative kinetic fitting of approach curves and line profiles. The method was first evaluated by fitting simulated data and then tested experimentally by resolving negative feedback and intermediate kinetics behavior in a spatially controlled fashion using (i) a flat, binary substrate composed of Au and SiO2 segments and (ii) a dual-mediator system for live-cell measurements. The methodology developed herein, named quantitative feedback referencing (QFR), improves fitting accuracy, removes fitting subjectivity, and avoids substrate–microelectrode contact.

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