G-Mode KPFM: Bringing Kelvin probe force microscopy into the information age

L. Collins, S. Kalinin, S. Jesse
Oak Ridge National Laboratory,
United States

Keywords: scanning probe microscopy, Kelvin probe force microscopy, electronic, electrochemical


Fundamental mechanisms of energy storage, corrosion, sensing, and multiple biological functionalities are directly coupled to electrical processes and ionic dynamics at both the solid-gas and solid-liquid interfaces. In many cases, these processes are spatially inhomogeneous (e.g., grain boundaries, step edges, point defects, ion channels), and possess complex time and voltage dependent dynamics. Kelvin probe force microscopy (KPFM), based on the century old Kelvin probe, has provided deep insights into the local electronic, ionic and electrochemical functionalities in a broad range of energy storage and conversion materials and devices. Despite the popularity of KPFM (~1800 publications), the level of information available is not sufficient for some systems such as electroactive materials and devices, or in systems containing electrolytes, where important time and bias dependent electrochemical phenomena need to be considered. Practically, the detection methodologies adopted in classical KPFM, and AFM in general, limit the time resolution of the measurement and hence mask many of the underlying dynamic processes of interest. In this presentation, the foundations are laid for a new era in functional imaging utilizing big data collection and analytics. General Acquisition mode (G-Mode) KPFM will be introduced and will be shown to allow extraction of dynamic information on the local electrochemical processes taking place with nanometer spatial resolution and sub 10 ┬Ás temporal resolution. Furthermore, G-Mode KPFM is immediately implementable on all AFM platforms, is shown to allow simultaneous capture of numerous channels of information simultaneously, as well as increased flexibility in terms of data exploration across frequency, time, space, and noise domains. We believe that such a symbiotic relationship between big data analytics and SPM will allow us to push the limits of existing approaches, revealing information on local dynamic processes previously inaccessible, and ultimately help realize knowledge driven optimization of battery materials, photovoltaic devices, and biomaterials amongst a myriad of other applications.