About the Project

Thin films with a high technical specification are used in many everyday devices, including displays, solar cells, electronic devices, batteries, and sensors. Printing of the high-value flexible electronic films with insulating, dielectric, semiconducting and conducting materials used in these devices makes a major and rapidly growing contribution to UK industry. The thickness of the films required, the starting materials used and the final high-value functions desired in the finished product vary significantly. However, the scientific principles that govern the behaviour of the printing processes for these diverse applications have many similarities, because they are all formed by selectively spreading a wet film of ink and drying it.

At present the optimisation of the printing parameters for these films is commonly achieved through a trial and error process rather than systematic intelligent control. Individual processes are being optimised in isolation without cross-fertilization of knowledge. In a fast changing world, where disruption to supply chains or development of improved materials can change the process input materials, the need to reconfigure the formulations/printing parameters used increases. Furthermore, desired outputs can also change rapidly as the manufacturers and customers seek to meet changing demands of their market for example requiring more precise control of film parameters such as thickness and electrical properties. Adjusting to such continually moving goal posts by relying on trial and error testing is time-consuming, wasteful and costly.

This project will develop responsive manufacturing technology that will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing process in real-time and therefore maintain an optimized printing process robustly in the face of variations in feedstock materials and/or the required output.  This will be achieved by developing novel printing components and control algorithms for the printing process that take into account our theoretical understanding of the processes occurring and utilizing high-speed in situ data acquisition to respond autonomously and continuously to perturbations in the feedstock materials, machine behaviour, or required film properties.

The project will use of the wide range of laboratory scale processing systems our project team regularly use for the production of model colloidal films, ceramic dielectrics, photovoltaics and battery electrodes to provide the datasets required to educate the machine learning algorithms.

The project will also look to develop novel coating tools and approaches which move beyond current state-of-the-art manufacturing to give next-level control over the coating process.

Applications for research programmes with synergies to the above described research are also welcomed. The applicant will work in a well-equipped laboratory space with access to state-of-the-art manufacturing and metrology systems.

How to apply:

Applicants should have a minimum of an upper second class honours degree in Physical Sciences and Engineering, such as Materials Science, Physics, Chemistry, Chemical Engineering or Mechanical Engineering. If English is not your first language then you must have an International English Language Testing System (IELTS) average of 6.5 or above with at least 6.0 in each component, or equivalent. Please see this link for further information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language.

Please see this link for information on how to apply: https://www.sheffield.ac.uk/cbe/postgraduate/phd/how-apply. Please include the name of your proposed supervisor and the title of the PhD project within your application.