Living on the Edge: Tunable Electronics from Edge Structures in 1D Layered Materials

Objectif

One of the driving forces of the ongoing nanotechnology revolution is the ever-improving ability to understand and control the properties of quantum matter even down to the atomic scale. Key drivers of this revolution are layered materials like transition metal dichalcogenides (TMD). The realisation of novel TMD-based electronic devices relies heavily on understanding the relation between structural and electrical properties at the nanoscale. Crucially, one-dimensional (1D) TMDs have been predicted to exhibit striking functionalities including metallic edge states, ferromagnetic behaviour, and mobilities that are not suppressed as compared to their 2D counterparts. Indeed, in the 1D nanoscale limit, the lateral edges of TMDs become dominant, opening novel opportunities to tune edge-induced electrical properties leading to i.e. enhanced charge carrier mobility.
However, these predictions for novel phenomena in 1D TMDs lack experimental verification, due to the challenge in accessing the relevant information at the nanoscale. I propose to unravel the interplay between structural and electrical edge-induced properties by exploiting recent breakthroughs in electron microscopy (EM) allowing simultaneous unprecedented spatial and spectral resolution. I will focus on MoS2 nanoribbons, and use electron-energy loss spectroscopy to map the electronic properties at the nanometer-scale. Beyond the optimization of EM for 1D TMD characterization, I will investigate semiconducting-to-metal and ferromagnetic transitions by realising controllable edge structures. I have an extensive track record in pushing the frontier of EM characterization and growing nanostructures. I recently demonstrated the feasibility of pinning down the interplay between structure and electronic properties at the edges of 2D MoS2. This proposal will provide input towards novel quantum technologies for developing low-energy-consumption tunable electronics, efficient signal processing and quantum computation.