In situ electronic characterization of electrochemically tunable metal oxide nanostructures for energy applications via inverted scanning tunneling spectroscopy

An in situ methodology was developed and implemented to characterize electronic band properties at the apex of high-aspect-ratio nanoscale solid-state electrochemical devices. Model devices with materials relevant to various energy applications were composed of nano-thin metal oxide films coated onto metallic scanning tunneling microscopy (STM) probe tips. Electronic properties of these films at the device apex were measured by inverting the tunneling spectroscopy paradigm: vacuum tunneling spectroscopy between functionalized STM probe tips with ambiguous electronic properties and metallic substrates – a method we here introduce as inverted scanning tunneling spectroscopy (I-STS). Additionally, concentration of metal species within the apex of these metal-oxide devices can be controlled through nanoscale solid-state electrochemistry within the same experimental platform, allowing subsequent in situ I-STS measurements that directly reveal how electronic properties change with composition. We first demonstrated and validated the I-STS approach by quantifying the bandgap of nano-thin aluminum oxide ( ca . 7.1 eV). Next, bandgaps (or lack thereof) of nano-thin stoichiometric lithium cobalt oxide (LCO, ca . 1.6 eV) and deeply sub-stoichiometric LCO (0 eV) were observed, directly measuring LCO band evolution along its delithiation-induced semiconductor-to-metal transition. I-STS reveals bandgaps at the nanoscale apex that are substantially narrower than bulk values, demonstrating pronounced modulation of electronic structure in electrochemically active oxide nanostructure termini. Ultimately, I-STS uniquely enables electrical characterization of small, high-aspect-ratio model devices at their apex – a spatial regime inaccessible with existing techniques – and provides a pathway to explore ion-coupled electronic phenomena in advanced energy storage and low-power neuromorphic computing devices using standard STM instrumentation.