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Device

Title Description Supervisor(s)
Biosensing with molecular nanoribbons

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DNA sequencing (sensing the order of bases in a DNA strand) is an essential step toward personalized medicine for improving human health. Despite recent developments, conventional DNA sequencing methods are still expensive and time consuming. Therefore, the challenge of developing accurate, fast, and inexpensive, fourth-generation DNA sequencing alternatives has attracted huge scientific interest. An alternative strategy involves measuring changes in the electrical properties of the membrane e.g. molecular nanoribbons containing a pore. This project will establish design principles to use molecular nanoribbons for a new generation of quantum devices for selective sensing of biospecies such as DNA. Sara Sangtarash, Nicholas Hine
Complex nanostructured materials for thermoelectric energy harvesting

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Two thirds of all energy we use is lost into heat during conversion processes, a loss which puts enormous pressure on the planet and energy sustainability. Thermoelectric materials convert waste heat into electricity and can provide a solution towards this problem. This project studies the electronic and thermal properties of complex electronic structure materials subject to a large degree of nanostructuring, which is believed to be the most promising way to achieve extremely high conversion efficiencies. Density function theory methods, as well as semiclassical and quantum transport methods are merged to explore the design space of new generation thermoelectric materials. Neophytos Neophytou, Julie Staunton
Machine learning and quantum theory for magnetic quasiparticles and their information storage potential

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Some topological magnetic structures can behave as quasiparticles and be manipulated to store information. The discovery of skyrmion-type particles was a huge leap forward opening up the prospect of new smaller and more efficient devices. In this project we will identify atomistic spin models by using machine learning tools on data from calculations of the fundamental quantum mechanics of the electrons and study them to discover what complex but stable magnetic objects can emerge in some choice materials. These developments have wider scope too – for modelling the thermal properties of magnetic phases and their response to applied fields for new solid state cooling applications. Julie Staunton, Albert Bartok-Partay
Simulating Surface Spectroscopy of Single Atom Magnets and Catalysts

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Functional materials for catalysis and magnetooptical applications often contain precious materials, the scarcity of which is a strong motivation for maximizing the efficiency of their use. Single atoms can act as catalysts (Single atom catalysts, SACs) or single atom magnets (SAMs) when stabilized on well-defined substrates. SAC/SAM materials are often studied with X-ray photoelectron spectroscopy and X-ray absorption spectroscopy, but the rich structure and overlapping features make these spectra hard to disentangle. This project will develop new simulation methodology to predict such complex spectroscopic signatures and, in collaboration with experimental partners, novel SAM and SAC materials will be characterized.

Reinhard Maurer, Julie Staunton

2D Material Heterostructures and novel Twistronic Devices

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This project explores the exciting and novel physics of multilayer structures built from 2D materials. 2DMs can undergo dramatic changes in their fundamental physical properties when they are combined into heterostructures, particularly if their lattices are misaligned: an example is how graphene becomes superconducting when the alignment of two layers is “twisted” by specific angles of a few degrees. This new field of “twistronics” explores how properties of 2D materials can be tailored for specific applications by stacking them together. We will harness unique capabilities of Linear-Scaling DFT to design 2DM heterostructures “ab initio” for future application in electronic devices that combining high performance and ultra-low power usage. There will be opportunities to use Machine Learning tools to accelerate these simulations, and to develop theoretical spectroscopy methods that enable prediction and interpretation of state-of-the-art experimental results. Nicholas Hine, Neil Wilson