Increasing energy demands, exhaustion of easily accessible oil resources and fears of climate change make renewable energy sources a necessity. Although it is evident that future power generation will result from a wide mix of technologies, photovoltaic cells have made astounding technical and commercial progress in recent years. Over the last decade renewable energy generation has been stimulated by tax concessions and feed-in tariffs. Large scale manufacturing of photovoltaics has benefited from this and progress along the learning curve necessary to achieve economies of scale in manufacture has been very rapid. However like all renewable energy sources today the cost per kWh of electricity from photovoltaics is greater than that generated by fossil fuels, although the gap has reduced quite dramatically in the last two years. The cost reductions in generation from photovoltaics have been achieved through innovative cell design, the use of lower cost materials, advances in power management electronics and lower profit margins. At the moment, >85% of new installations use wafered silicon cells of multi-crystalline or single crystal material. In these cases a key issue has been developing technologies which use thinner slices (using less silicon for a given area of solar panel) and moving to "solar grade" silicon. This type of silicon is less pure than the electronic grade used for integrated circuits and is cast into multi-crystalline ingots but it is very much cheaper. This is an important issues because before these developments as much as 50% of the cost of a cell could be attributed to the silicon material. An important cost reduction per kWh delivered has been achieved in this way despite solar grade silicon producing cells of lower conversion efficiency than electronic grade material. Further substantial reductions in cost could be achieved by using silicon produced by less energy hungry metallurgical processes, for example starting the manufacturing process by the reduction of quartz with carbon and applying low energy purification processes. This type of silicon, known as upgraded metallurgical silicon, is even less pure containing compensated dopants and metals which can act as important recombination centres so reducing the efficiency further. The aim of this proposal is to develop methodologies which are able to bring the efficiency of cells made from these cheap forms of silicon close to the efficiencies achieved from the higher cost electronic grade material. This could increase the efficiency of multi-crystalline solar grade silicon by around 5% absolute and even more in the case of upgraded metallurgical silicon. Current silicon cell structures work well because hydrogen (usually from the silicon nitride antireflection layer) passivates surfaces and bulk defects. In electronic grade single crystal this reduces recombination to insignificant levels. It doesn't work as well in solar grade multi-crystalline silicon or upgraded metallurgical silicon because there are regions, sometimes entire crystal grains, which are not passivated by the hydrogen. However other regions are of very high quality often as good as electronic grade silicon. We associate the resistance to passivation with specific types of defect observed in lifetime maps of slices. In this project we plan to identify the defects which show resistance to hydrogen passivation by using electronic and chemical techniques (carrier lifetime, Laplace deep level transient spectroscopy, SIMS, Raman spectroscopy and defect modeling). The key part of the proposal is to use our knowledge of defect reactions in silicon to develop alternative passivation chemistries which can be applied, during slice or cell production, to those defect species resistant to hydrogen passivation. In this way we would expect to make a very important improvement to the efficiency of the dominant solar PV technology.

This Centre for Doctoral training in Industrially Focused Mathematical Modelling will train the next generation of applied mathematicians to fill critical roles in industry and academia. Complex industrial problems can often be addressed, understood, and mitigated by applying modern quantitative methods. To effectively and efficiently apply these techniques requires talented mathematicians with well-practised problem-solving skills. They need to have a very strong grasp of the mathematical approaches that might need to be brought to bear, have a breadth of understanding of how to convert complex practical problems into relevant abstract mathematical forms, have knowledge and skills to solve the resulting mathematical problems efficiently and accurately, and have a wide experience of how to communicate and interact in a multidisciplinary environment. This CDT has been designed by academics in close collaboration with industrialists from many different sectors. Our 35 current CDT industrial partners cover the sectors of: consumer products (Sharp), defence (Selex, Thales), communications (BT, Vodafone), energy (Amec, BP, Camlin, Culham, DuPont, GE Energy, Infineum, Schlumberger x2, VerdErg), filtration (Pall Corp), finance (HSBC, Lloyds TSB), food and beverage (Nestle, Mondelez), healthcare (e-therapeutics, Lein Applied Diagnostics, Oxford Instruments, Siemens, Solitonik), manufacturing (Elkem, Saint Gobain), retail (dunnhumby), and software (Amazon, cd-adapco, IBM, NAG, NVIDIA), along with two consultancy companies (PA Consulting, Tessella) and we are in active discussion with other companies to grow our partner base. Our partners have five key roles: (i) they help guide and steer the centre by participating in an Industrial Engagement Committee, (ii) they deliver a substantial elements of the training and provide a broad exposure for the cohorts, (iii) they provide current challenges for our students to tackle for their doctoral research, iv) they give a very wide experience and perspective of possible applications and sectors thereby making the students highly flexible and extremely attractive to employers, and v) they provide significant funding for the CDT activities. Each cohort will learn how to apply appropriate mathematical techniques to a wide range of industrial problems in a highly interactive environment. In year one, the students will be trained in mathematical skills spanning continuum and discrete modelling, and scientific computing, closely integrated with practical applications and problem solving. The experience of addressing industrial problems and understanding their context will be further enhanced by periods where our partners will deliver a broad range of relevant material. Students will undertake two industrially focused mini-projects, one from an academic perspective and the other immersed in a partner organisation. Each student will then embark on their doctoral research project which will allow them to hone their skills and techniques while tackling a practical industrial challenge. The resulting doctoral students will be highly sought after; by industry for their flexible and quantitative abilities that will help them gain a competitive edge, and by universities to allow cutting-edge mathematical research to be motivated by practical problems and be readily exploitable.