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Mathematical modelling of (perovskite) solar cells

Project Description

Applications are invited for a fully-funded three year PhD to commence in October 2019.

The PhD will be based in the School of Mathematics and Physics and will be supervised by Dr Jamie Foster, Dr Marianna Cerasuolo and Professor Andrew Osbaldestin.

Project description
The work on this project will carry out the mathematical modelling and analysis required to underpin and inform changes in the current design of perovskite solar cells to increase their usable lifetime.

This is an opportunity to work in an extremely active area; and one that is forecast to experience significant growth over the coming decades. As such, this is an excellent opportunity to make a strong start in pursuing a career in research. It is expected that the candidate will graduate with publications in high-impact journals and will therefore be well-placed to continue their upward trajectory in science.

Developing efficient means of renewable energy capture is key to the low-carbon economy. The past few years have seen an explosion of interest in perovskite-based solar cells (PSCs). This young technology recently surpassed market-leading silicon technology by achieving an efficiency of 23%. They are also made from cheap and clean materials. The largest roadblock that remains to PSC commercialisation is their long-term durability. At present, PSCs can maintain usable performance for several months, but in order to compete at market this needs to be extended.

The aims of this project are to carry out the mathematical modelling and analysis required to inform changes in the cell design to mitigate processes associated cell degradation. Models must be coarse enough to capture the important phenomena occurring throughout the device, yet must retain sufficient detail so that the underlying physics can be interrogated. Drift-diffusion models provide this middle-ground and will be the central approach used in the project.

Models will be solved using a combination of asymptotic and numerical techniques. They will then be iteratively refined, by comparison with real-world data provided by experimental collaborators, until reliable predictive power is established. Ultimately, they will be used to identify optimal designs for cells that not only give rise to high initial efficiencies, but ones that are able to maintain this performance in the long-term.

General admissions criteria
You’ll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements
We’d welcome applications from candidates with some knowledge of the physics of semiconductors as well as some familiarity with asymptotic methods and scientific computing/numerical methods.

How to Apply
We’d encourage you to contact Dr Jamie Foster () to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form and select ‘Mathematics and Physics’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this funded PhD opportunity you must quote project code MPHY4440219 when applying.

Funding Notes

Successful applicants will receive a bursary to cover tuition fees for three years and a stipend in line with the RCUK rate (£15,009 for 2019/2020). The Faculty of Technology may fund project costs/consumables up to £1,500 p.a.

How good is research at University of Portsmouth in Mathematical Sciences?

FTE Category A staff submitted: 11.00

Research output data provided by the Research Excellence Framework (REF)

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