Dr N Neophytou
Supervisor: Dr Phytos Neophytou.
Electronic and thermoelectric transport in highly heterogeneous nanomaterials and devices
Two-thirds of all energy we use is lost into heat during conversion processes, a loss which puts enormous pressure on the plant, the use of fossil fuels, and energy sustainability.
Phonon transport in nanostructured and low-dimensional materials
Heat transport in nanostructures, whose feature sizes can vary from a few up to 100s of nanometers, exhibit distinctly different behaviour compared to heat transport in bulk materials. There is a large ongoing effort to explore this unusual behaviour in order to control heat flow for heat management applications, thermoelectric energy harvesting applications, sensors, thermal diodes, thermal cloaking, thermal insulation, etc., with envisioned efficiencies that could exceed those of conventional bulk materials. This newly emerging field is referred to us ‘phononics’, attributed to phonons, the fundamental elements of heat transfer. The goal of this PhD project is to understand phonon transport in nanostructured and low-dimensional materials by exploring both their particle and wave nature through theory and (large-scale) simulations. A variety of theoretical models and simulation approaches will be developed and used to describe phonon transport, spanning from employing the simplified Callaway model, to the particle-based Monte Carlo approach, and to the wave-based Non-Equilibrium Green’s Function approach. Each model explores a different phonon transport regime and different phenomena. The unified phonon transport computational framework that will be developed during the project will provide understanding to a large variety of open problems related to nanoscale heat transfer.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.
Computational Design of Nanomaterials
Nanotechnology offers exciting new possibilities for the design of new materials with desired, 'engineered', properties. Applications for this span through a variety of fields, from a new generation of transistor devices, materials for energy conversion and generation such as advanced solar cells and thermoelectrics, nanosensors for ultra-high sensitivity, optoelectronics and solid state lighting, and so forth.
This project provides a unique opportunity to perform theoretical research through the use of atomistic and quantum mechanical computational methods, to explore the optimal design of material properties at the nanoscale. It will provide the opportunity to the student to use and develop advanced theoretical methods and computational tools that can be used for the design of new nanomaterials, guide the works of experimentalists, and open the path for new and exciting applications. This PhD project will be aligned with a larger effort to understand electrothermal properties at the nanoscale that will be initiated within a European Research Council (ERC) funded project.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.
Thermoelectric transport in nanocomposite materials: Modeling and simulation
Thermoelectric (TE) devices, that convert waste heat flow into useful electrical power, are recently receiving large attention due to the increasing importance of energy savings. Thermoelectrics, however, have traditionally only found use in niche applications for reasons of low efficiency and high prices. The ability of a material to convert heat into electricity is measured by the dimensionless figure of merit ZT =σS2T/(κe+κl), where σ is the electrical conductivity, S is the Seebeck coefficient, and κe and κl are the electronic and lattice part of the thermal conductivity, respectively.
Over the past five decades, it has been challenging to increase ZT > 1 because the parameters that control it are generally interdependent and optimizing one parameter often adversely affects another. Recently, large performance improvements were demonstrated in nanostructured thermoelectric materials. Nanotechnology offers the possibility to independently design the parameters that control ZT, such that high performance is achieved.
The goal of this PhD thesis is to theoretically investigate thermoelectric transport in nanocomposite materials and more specifically investigate ways to achieve large thermoelectric power factors. The feature sizes in these materials can vary from a few to 100s of nanometers. A modelling and simulation approach will be followed, involving electronic and heat transport in nanocomposite materials. The thesis
has the following objectives: i) to illuminate the effects of the nanocomposite geometry on energy filtering and on the Seebeck coefficient, ii) to investigate possible ways to relax the interdependence between the electrical conductivity and the Seebeck coefficient such that high power factors are achieved, and iii) to investigate the possible influence of quantum mechanical effects on the transport of electrons in these geometries and their influence on the thermoelectric properties. The project involves solid state physics, simulator development, computation, and material science.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.
Modelling the thermal conductivity of porous silicon nanomeshes for thermoelectric applications
Nanoporous membranes made of single-crystalline Si (also referred to as nanomeshes) are promising candidates for thermoelectric materials as they can provide extremely low thermal conductivity. The structure consists of a crystalline Si membrane that includes pores of a certain diameter, pitch, and porosity, either disordered or in ordered arrays. The feature sizes can extend from 10 to a few 100s of nanometers. The modelling of novel thermoelectric devices as such requires a treatment of heat transport that goes beyond the simple diffusion equation. Thermal transport in
such structures requires the solution of the Boltzmann transport equation (BTE) for phonons, usually using the Monte Carlo method. In this method, the particle nature of phonons is considered. Significant experimental evidence, however, indicates that strong coherent wave phonon effects exist in such materials, which influence dramatically the thermal conductivity.
The goal of this PhD thesis is to theoretically investigate thermal transport in nanomeshes in which the pores are placed in a regular or an irregular fashion, through advanced modelling and simulation techniques. The thesis has the following objectives: i) to illuminate the effects that originate from the particle versus the wave nature of phonons as they flow through the nanomeshes, ii) to investigate coherent phonon effects
on the phonon modes, and iii) to identify strategies that dramatically reduce the thermal conductivity in these materials (at a given material porosity), aiming to the design of better thermoelectric materials. The project involves solid state physics, simulator development, computation, and material science.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.
Inverse design and machine learning techniques applied to thermoelectric nanomaterials
Thermoelectric materials convert directly heat into electricity, and they are very promising in contributing towards energy savings, reduction of the use of fossil fuels, powering autonomous sensors, etc. Nanostructured thermoelectric materials have recently emerged as an advanced technology with much higher performance and lower prices compared to existing bulk materials. The design and optimization of nanostructures, however, is not a trivial task, and involves understanding electronic and phonon transport phenomena at the nanoscale, such as ballistic to diffusive and quantum mechanical to the semiclassical crossovers. In addition, the atomistic details of nanostructures, the details of the geometry, and the details of different material compositions determine the thermoelectric properties at large. Furthermore, the parameters that optimize thermoelectric transport are adverse interrelated, and optimizing one parameter usually negatively affects another.
This PhD project creates and utilizes inverse design machine learning techniques to assist the design of next generation nanostructured thermoelectric materials. It will be aligned with a larger effort within a European Research Council (ERC) funded project and will use electrothermal transport tools that will be developed within that project. It will provide automated performance optimization, achieved through the use of the Markov Chain Monte Carlo (MCMC) technique to nanostructured materials. This will be used within the Adaptive Sequential Monte Carlo (ASMC) scheme, which can provide parallelisation in the optimization process. To speed up the optimization procedure, a well calibrated Gaussian Process (GP) surrogate will be used to emulate the functionality of existing electrothermal simulators, and then the surrogate will be used within the ASMC algorithm.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.
Nanotechnology offers exciting new possibilities for the design of new materials with desired, ‘engineered’, properties. Applications for this span through a variety of fields, from new generation of transistor devices, materials for energy conversion and generation such as advanced solar cells and thermoelectrics, nanosensors for ultra-high sensitivity, optoelectronics and solid state lighting.
This project provides a unique opportunity to perform theoretical research through the use of atomistic and quantum mechanical computational methods, to explore the optimal design of material properties at the nanoscale. It will provide the opportunity to the student to use and develop advanced theoretical methods and computational tools that can be used for the design of new nanomaterials, guide the works of experimentalists, and open the path for new and exciting applications.
Note: Should your application for admission be accepted you should be aware that this does not constitute an offer of financial support. Please refer to the scholarships & funding pages.