- Scanning Probe Microscopy
- Liquid/solid interfaces
- Big data and Machine Learning
Scanning Probe Microscopy
We are very active in the simulation of Scanning Probe Microscopy (SPM), particulary Atomic Force Microscopy (AFM), Scanning Tunneling Microscopy (STM) and Kelvin Probe Force Microscopy (KPFM). In general, experimental studies of surfaces with atomic resolution are often difficult to interpret and simulations provide a link between measured images and surface topography and electronic structure. SPM techniques can be applied to the study of a wide variety of systems, including ideal and defective surfaces, molecular adsorbates, organic films, DNA, proteins, and the corresponding theoretical toolbox is equally wide.
Surface science techniques, and particularly high-resolution Scanning Probe Microscopy (SPM) approaches, now offer unprecedented levels of understanding and control of solid/vacuum interfaces. By contrast, the physics of liquid/solid interfaces is less developed, although it is often more relevant for real-world applications. It is important in such diverse fields as heterogeneous catalysis, next generation battery technology and corrosion. The solid/liquid interface is also particularly relevant to biological systems, where measurements are made in physiological conditions. In this work we apply a combination of first principles and atomistic simulation approaches to study how liquids interact with a variety of insulating and organic surfaces, providing atomic-scale insight into hydration structures, dissolution, friction and high-resolution imaging.
“Big data” and “Machine Learning” have become buzz words in information technology. Big data refers to data sets that are so large that traditional ways of human information processing can no longer process them. Instead, machine learning techniques are being developed for computer-aided data processing, and they now permeate ever growing parts of society, industry and market economies. In collaboration with computational science groups and international data repositories, we are developing the infrastructure to gather, analyse and mine data produced by entire materials science communities – building up the technology to analyze large data sets (big data) and create the tools that add value (materials informatics and machine learning). Current focus areas are in the parametrisation of interaction potentials, optimization of lubricants, structural characterization of interfaces and visualization of complex defects. Our efforts are coupled with the Aalto Institute of Science and the NoMaD European data repository initiative.
Nanoelectronics As the scaling of the CMOS devices approaches its technological and fundamental limits, the replacement of silicon and its native oxide becomes one of the key challenges to sustain the continuous improvement of the IC performance. New semiconductors and dielectrics are at the heart of development of new and emerging high performance nanoelectronic devices. We use multiscale atomistic modelling to study the structure of interfaces and probable defects in next-generation materials, including devices based on 2D materials and molecular assemblies. This work also involves the development of methods for simulating thousands of atoms with high accuracy.
Nanomanipulation Scanning Probe Microscopy (SPM) has become a dominant tool for the design and activation of nanodevices. SPM direct mechanical manipulation can be used to build nanosystems piece by piece. It relies on locally changing the migration barriers by close-approach with the SPM tip pushing or pulling the molecule. This offers local control of molecular properties, and is in principle viable for any system. More importantly, SPM provides in situ high-resolution characterization, allowing direct observation of the dynamics even at the atomic scale. As well as being a tool for nanomanipulation, SPM provides a direct measure of the energy dissipated at the nanocontact between tip and cluster, and offers insight into the energy loss due to friction during manipulation. In a pioneering study, we recently showed that it is possible to identify the chemical stages of the reaction of water with a salt surface by calculating the adsorption and diffusion barriers of various complexes and comparing them to manipulation experiments. We aim to extend this to other molecules and materials, and show that nanomanipulation can be used to characterize the reaction environment and even control the reaction itself. In parallel to our studies of the catalytic properties of metal nanoclusters on insulators, we study the lateral mobility and dissipation of nanoclusters adsorbed on surfaces. The nanoclusters also represent prototypical systems for the study of electron transport phenomena in nanostructures. Manipulation can be used to form structures of choice, such as ordered arrays of metallic nanoclusters acting as metallic nanowires, with their transport properties controlled by the cluster size and the cluster density. The current techniques available for controlling size, charge and adsorption site of deposited metallic nanoclusters provides a large investigative space for systematically studying the influence of these factors on cluster mobility and dissipated energy.
Nanotribology The study of friction remains an important element in the development of many industrial and technological processes, which are wear-dependent. As the components of technologies are reduced in size, then the resolution of understanding must also increase. We intend to understand friction and wear properties in the extreme case of atomic scale friction, were only a few atoms constitute the tip-sample contact. Despite recent successes in the understanding atomic friction processes, where the velocity dependence, load dependence and new effects like superlubricity (structural and externally induced) have been targeted, many properties are still heavily under dispute. At this point, a multitude of experimental and theoretical work exists, however, only a few papers report on the direct overlap of experiments and theory. Atomic scale friction is particularly well suited for direct comparison, since the contact size is as small as possible, and thus much better defined then in conventional tribology experiments. This invites direct comparison of atomic friction experiments with molecular dynamics simulations (MD) based on discrete atom geometries. We are particularly interested in recent developments in using different oscillation modes of an AFM to probe lateral forces on surfaces – torsional resonance mode. We are also part of a large European Network studying friction at the nanoscale, COST MP1303.