PhD Vacancy
This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute. Perovskite solar cells (PSCs) represent one of the most significant breakthroughs in materials science over the last decade. These synthetic materials, offer light-absorption properties and charge-carrier mobilities that now rival conventional silicon. However, the primary barrier to their global adoption is the "scale-up gap." While small-scale lab devices show exceptional efficiency, maintaining that performance during high-speed, industrial manufacturing remains a significant challenge.
This PhD project focuses on the transition of PSCs from the lab to commercial-scale production. In collaboration with Power Roll Ltd, you will work on a unique architecture: their v-groove, back-contact device technology. Unlike standard flat-panel solar cells, Power Roll’s design uses thousands of micro-grooves in which each groove acts as an individual solar-cell device [1,2]. While this design is innovative, cost-effective and eliminates expensive materials, it introduces additional physical requirements during the device fabrication process.
This project is based around roll-to-roll (R2R) slot-die coating; a process similar to newspaper printing but requiring nanometer-level precision. At industrial speeds, the quality of the perovskite film is dictated by an intricate web of variables, including, ink rheology (how the liquid precursor flows under pressure), film drying kinetics (the rapid transition from a wet film to a solid crystal) and fluctuations in local temperature, web speed, and pump pressure etc.
Because these variables interact in non-linear ways, traditional "first-principles" models often struggle to predict the outcome. Your task will be to integrate a suite of optical characterization tools (e.g. measuring absorption and photoluminescence) directly into a prototype R2R printing line. This "on the fly" collected-data, combined with structural analysis like scanning electron microscopy, will form the basis for Machine Learning models.
The ultimate goal is to move beyond trial-and-error, creating an autonomous system capable of identifying the optimal printing conditions to maximize efficiency and yield. You will be supervised by Prof David Lidzey at the University of Sheffield, School of Mathematical and Physical Sciences. Prof Lidzey is an internationally recognized leader in the field of organic and hybrid electronics, and leads an active, friendly and supportive research group working on perovskites and other thin-film optoelectronic technologies.
More details about our group can be found here .