PhD, MPhil Chemical & process engineering

Filtration and washing of pharmaceutical products is classically undertaken empirically. This project will use nano-computed tomography (nano-CT), a high-resolution cross-sectional imaging technique to track the progress of washing at high resolution in real time.

This modelling and simulation study will investigate the complex processes that occur when foam, rather than water, is used as a carrier for fibres during papermaking, with a view towards reducing the overall water footprint of the paper production process.

This project aims to develop a new computational tool, combining molecular simulations and machine learning approaches, to predict the solubility of complex pharmaceutical molecules in a variety of solvents.

Focussed on the development of novel sorption systems for water remediation, the project combines materials development, characterisation and testing for a range of pollutants, including pharmaceuticals and heavy metal species.

The project aims to address the issues related to low capacity and poor kinetics of sorbents used for post combustion carbon dioxide capture applications.

The proposed project aims to develop new technology for reforming syngas hydrocarbons to promote hydrogen production from biomass, it addresses the issues of global importance, which are committed to making a useful and positive contribution to clean energy challenges locally and globally.

This project will undertake studies on the processes and technology required to convert biomass waste into a range of hydrocarbons. This research project requires both modelling and experimental studies to analyse the waste to energy process and the fuel properties of hydrocarbons and chemicals produced.

The purpose of this project is to apply a formulation to polymeric turbulent boundary layer flows and, in this way, to make some first, yet decisive, steps in the physics and algorithmics of fully-coupled polymeric boundary layer turbulence.

Many applications rely on structured films and membranes, such as selective filters, adsorbents, and biological systems. The project will use computer modelling to analyse how particles with complex interactions aggregate to form films with complex structure and complex time-evolution.

The project will focus on the applications of latest Small-angle X-ray scattering system in CMAC (EPSRC Centre of excellence in continuous manufacturing and crystallisation) to provide mechanistic understanding of interparticle interaction in dense system under repulsive or attractive interactions.

This work will use a combination of machine learning and molecular thermodynamic modelling to develop a methodology to efficiently develop accurate mathematical representations of the thermodynamics of complex multicomponent mixtures, with a focus on the pharmaceutical industry.

This project will develop improved theories of electrochemical reactions by combining recent advances in the description of the electric double layer with those in electron transfer kinetics.

This project aims to develop a new multi-scale simulation model to describe the entire synthesis process of bio-inspired silica materials. The model will then be used to design an ideal material for carbon capture applications.

This modelling and simulation study will investigate processes that occur when foam injection is used to displace fluids from an oil reservoir, and in particular what happens as the foam front changes direction as additional injection wells are brought online.

This project will investigate behavioural and performance characteristics of students when working in Problem-Based and Enquiry-based learning activities in order to elucidate the link between the use of these approaches and the development of learner’s autonomy.

This project aims to develop next-generation waste valorisation and waste-to-energy approaches for organic waste treatment using a lab-scale experimental approach and mathematical models.

To enhance equitable energy access and reduce global emissions from energy generation, this project will focus on developing a clean energy technology based on waste utilisation and thermochemical conversion (e.g. gasification, combustion, pyrolysis or hybrid) approaches.

Quantum Chemistry and Molecular Dynamics calculations will be carried out to explore the mechanism and kinetics of lignocellulosic biomass processing and the conversion of waste into valuable products.

In this project, an integrated system will be designed and developed to ensure efficient generation of liquid transportation fuels whilst maximising the value of biomass into value-added chemicals by controlling structure and functionality as they form.

The project involves the development of a pilot scale reactor with instrumentation that will enable the interrogation and optimisation an electroforming process. Both scientific and statistical techniques will be employed to interpret process data enabling process control.

This project aims to develop embedded optical fibre sensors for polymer electrolyte membrane fuel cells to characterise catalyst degradation mechanisms under realistic operating conditions.

Redox flow batteries conventionally employ toxic and corrosive transition metals such as vanadium. This project will develop neutral aqueous organic redox electrolytes suitable for high performance flow batteries with benign chemical properties in terms of safety, cost and end-of-life recycling.

This PhD project will explore the design, characterisation and application of exsolved materials for power-to-X energy conversion devices where X can be fuels, chemicals, heat or power, all essential for transitioning to a clean, sustainable energy economy.

This PhD project will develop reversible solid oxide cells based on a newly discovered class of materials, so called exsolved or emergent materials.

The project will investigate Machine Learning and Deep Learning approaches to extract and fuse multiple data streams from multi-sensor setups in chemical and pharmaceutical manufacturing through a combination of experimental work, data analytics and process simulation.

The project will explore the use of hybrid models that combine the advantages of data-driven and physics-based first principles models to enhance the performance of current models for crystal nucleation and growth in chemical and pharmaceutical manufacturing.

This research will investigate the effect of supported nanopaticles size on the reactivity of nanocomposite materials in heterogeneous reactions. The fundamental questions are how the reactivity scales with the nanoparticles size and whether the stoichiometry of the reaction is affected.

This projects involves the application of laser imaging techniques to study soot formation. The particular interest is in flames where hydrogen is blended with hydrocarbon fuel. The ultimate goal is improved understanding (and thus minimisation) of pollutant formation.

The research is a co-operation with researchers at California Institute of Technology, and will pioneer superfluid flow-structure interactions with are of key importance in technological and astronomical applications.

We will develop an innovative technique to study crystal nucleation, using an optical tweezer to generate a localized shear flow in a crystallizing solution, thus triggering nucleation in a known position enabling direct measurement of the growing nucleus.

This PhD project will explore the design, characterisation and application of new materials for electrochemical power-to-X conversion (X = fuels, chemicals) devices essential for transitioning to a clean, sustainable energy economy.

Employ finite volume solvers for turbulent and creeping flows in conjunction with stochastic differential flow solvers to model directly mesoscopic and macroscopic ferrofluid flows.

This project aims to apply molecular simulation methods to study gas separation from mixtures containing water in novel Metal-Organic Framework (MOF) materials. New molecular models will be developed to improve adsorption predictions with application in carbon capture from humid streams.

The project will explore the use of Machine Learning for the analysis and fusion of data from multiple sensors in agricultural applications. Satellite and drone multispectral imagery together with ground sensors will inform decision support tools for advanced farm management.

In this project, the student will be involved in an industrial funded project to develop and use spatially offset Raman spectroscopy (SORS) and spatially resolved diffuse reflectance spectroscopy (SR-DRS) for obtaining insight into sub-surface powder drying behaviour.