Abstract
From hybrid perovskites to semiconducting polymer/fullerene blends for organic photovoltaics, many new materials being explored for energy harvesting and storage exhibit performance characteristics that depend sensitively on their nanoscale morphology. At the same time, rapid advances in the capability and accessibility of scanning probe microscopy methods over the past decade have made it possible to study processing/structure/function relationships ranging from photocurrent collection to photocarrier lifetimes with resolutions on the scale of tens of nanometers or better. Importantly, such scanning probe methods offer the potential to combine measurements of local structure with local function, and they can be implemented to study materials in situ or devices in operando to better understand how materials evolve in time in response to an external stimulus or environmental perturbation. This Account highlights recent advances in the development and application of scanning probe microscopy methods that can help address such questions while filling key gaps between the capabilities of conventional electron microscopy and newer super-resolution optical methods. Focusing on semiconductor materials for solar energy applications, we highlight a range of electrical and optoelectronic scanning probe microscopy methods that exploit the local dynamics of an atomic force microscope tip to probe key properties of the solar cell material or device structure. We discuss how it is possible to extract relevant device properties using noncontact scanning probe methods as well as how these properties guide materials development. Specifically, we discuss intensity-modulated scanning Kelvin probe microscopy (IM-SKPM), time-resolved electrostatic force microscopy (trEFM), frequency-modulated electrostatic force microscopy (FM-EFM), and cantilever ringdown imaging. We explain these developments in the context of classic atomic force microscopy (AFM) methods that exploit the physics of cantilever motion and photocarrier generation to provide robust, nanoscale measurements of materials physics that are correlated with device operation. We predict that the multidimensional data sets made possible by these types of methods will become increasingly important as advances in data science expand capabilities and opportunities for image correlation and discovery.
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