Periodic Reporting for period 4 - TESLA (Living on the Edge: Tunable Electronics from Edge Structures in 1D Layered Materials)
Reporting period: 2023-07-01 to 2024-06-30
One of the main 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 such as transition metal dichalcogenides (TMD). The realisation of novel TMD-based electronic devices relies heavily on understanding the relation between their 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.
This proposal will provide input towards novel quantum technologies for developing low-energy consumption tunable electronics, efficient signal processing and quantum computation. These applications are directly relevant to address important societal challenges, such as to reduce the energy demand of the computing and IT sectors.
The overall objective of the project is 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. Specifically, the two main goals of the project can be classified as follows:
a. Inducing novel functionalities in 1D TMDs by tuning edge structure configurations. Here the aim is to investigate how the electronic and magnetic states are modified through controllable edge structures (AC, ZZ, and their combination), chemical functionalization and vacancy-induced localized gap states.
b. Mapping the interplay between edge-induced structural, electric, and magnetic properties of 1D TMDs. The goal is to pin down the unique physical properties of the edges of 1D ZZ (AC) MoS2 nanoribbons such as metallic edge states, the tunable band gap, and the ferromagnetic order. This will be achieved by optimizing low-voltage electron energy-loss spectroscopy (EELS) measurements, including momentum-resolved EELS, to access the local band structures and dispersion relations within nanoscale volumes.
This proposal will provide input towards novel quantum technologies for developing low-energy consumption tunable electronics, efficient signal processing and quantum computation. These applications are directly relevant to address important societal challenges, such as to reduce the energy demand of the computing and IT sectors.
The overall objective of the project is 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. Specifically, the two main goals of the project can be classified as follows:
a. Inducing novel functionalities in 1D TMDs by tuning edge structure configurations. Here the aim is to investigate how the electronic and magnetic states are modified through controllable edge structures (AC, ZZ, and their combination), chemical functionalization and vacancy-induced localized gap states.
b. Mapping the interplay between edge-induced structural, electric, and magnetic properties of 1D TMDs. The goal is to pin down the unique physical properties of the edges of 1D ZZ (AC) MoS2 nanoribbons such as metallic edge states, the tunable band gap, and the ferromagnetic order. This will be achieved by optimizing low-voltage electron energy-loss spectroscopy (EELS) measurements, including momentum-resolved EELS, to access the local band structures and dispersion relations within nanoscale volumes.
The TESLA project, titled "Living on the Edge: Tunable Electronics from Edge Structures in 1D Layered Materials," aimed to explore and harness the unique properties of ultra-thin, low-dimensional materials known as transition metal dichalcogenides (TMDs). These materials hold great promise for next-generation electronic and photonic devices due to their ability to exhibit extraordinary behaviors at the atomic scale, particularly at their edges.
Throughout the project, we developed innovative techniques for fabricating and analyzing these materials, allowing us to manipulate their structures at a scale of mere nanometers—thousands of times thinner than a human hair. One of the key achievements was the successful creation of vertically aligned MoS2 nanostructures and Mo/MoS2 core-shell nanopillars with precise control over their size and shape. These structures demonstrated enhanced optical and electronic properties, paving the way for potential applications in areas such as nanophotonics, where light is manipulated on a tiny scale.
In addition to these fabrication advancements, we also made significant strides in understanding how strain—stretching or compressing the material—can alter its electronic properties. By developing a tool called StrainMapper, we could map and analyze the strain within these materials with unprecedented accuracy, revealing how strain affects their behavior and how it can be used to tailor their properties for specific applications.
Another major outcome of the project was the development of EELSfitter, an open-source software tool that applies advanced machine learning techniques to analyze data from electron microscopes. This tool has already become widely used in the scientific community, enabling researchers to gain deeper insights into the electronic properties of nanomaterials.
The results of the TESLA project have been widely disseminated through scientific publications, conferences, and workshops, and the tools we developed have been made freely available to the global research community. These achievements not only advance the field of nanomaterials but also lay the groundwork for future innovations in technology, from more efficient electronics to advanced sensors and beyond.
Throughout the project, we developed innovative techniques for fabricating and analyzing these materials, allowing us to manipulate their structures at a scale of mere nanometers—thousands of times thinner than a human hair. One of the key achievements was the successful creation of vertically aligned MoS2 nanostructures and Mo/MoS2 core-shell nanopillars with precise control over their size and shape. These structures demonstrated enhanced optical and electronic properties, paving the way for potential applications in areas such as nanophotonics, where light is manipulated on a tiny scale.
In addition to these fabrication advancements, we also made significant strides in understanding how strain—stretching or compressing the material—can alter its electronic properties. By developing a tool called StrainMapper, we could map and analyze the strain within these materials with unprecedented accuracy, revealing how strain affects their behavior and how it can be used to tailor their properties for specific applications.
Another major outcome of the project was the development of EELSfitter, an open-source software tool that applies advanced machine learning techniques to analyze data from electron microscopes. This tool has already become widely used in the scientific community, enabling researchers to gain deeper insights into the electronic properties of nanomaterials.
The results of the TESLA project have been widely disseminated through scientific publications, conferences, and workshops, and the tools we developed have been made freely available to the global research community. These achievements not only advance the field of nanomaterials but also lay the groundwork for future innovations in technology, from more efficient electronics to advanced sensors and beyond.
The next steps for the remaining of the project are expanding the study of edge-induced properties to other types of TMD-materials including WS2. We have also initiated, and plan to pursue further in the next months, studies of the low-temperature properties of one-dimensional TMD materials. For these materials, studies on the local electronic and structural properties along their edges will be carried out. Along this direction, we are close to starting the functionalization of the edges for tuning the physical properties of these materials. Other fabrication strategies have also led to interesting preliminary results, in particular to TMD-based nanowire architectures, which represent state-of-the art in this field.
Through the exploitation of advanced electron microscopy characterization techniques, we have initiated a new line of research for interpreting and understanding EM data by means of machine learning techniques inspired in strategies used in high-energy physics. The next step in this line of research following P6, which will be presented in a publication in the next months, is the demonstration of the spatially-resolved determination of the bandgap and complex dielectric function of van der Waals nanostructured materials from EELS spectral images. This successful strategy will be subsequently extended to the automated feature identification in EEL spectra, such as the position and width of plasmon and exciton peaks in nanomaterials, and assessing their variation across the specimen. All these results will be implemented in the public ML-based open-source code EELSfitter, which represents state-of-the-art for data analysis and interpretation in TEM-EELS measurements.
Through the exploitation of advanced electron microscopy characterization techniques, we have initiated a new line of research for interpreting and understanding EM data by means of machine learning techniques inspired in strategies used in high-energy physics. The next step in this line of research following P6, which will be presented in a publication in the next months, is the demonstration of the spatially-resolved determination of the bandgap and complex dielectric function of van der Waals nanostructured materials from EELS spectral images. This successful strategy will be subsequently extended to the automated feature identification in EEL spectra, such as the position and width of plasmon and exciton peaks in nanomaterials, and assessing their variation across the specimen. All these results will be implemented in the public ML-based open-source code EELSfitter, which represents state-of-the-art for data analysis and interpretation in TEM-EELS measurements.