DTA - Large volume 3D imaging of defects in battery and energy production materials

Multimodal three-dimensional (3D) data are transforming our ability to investigate, design and predict the behaviour of a wide range of materials in life, earth and materials sciences. This capability has been used in Connectomics to map the connectivity and neural network in the brain, to explore the chemical segregation, defect, crack propagation and porosity and their relationship with the matrix in engineering materials and geological ores. Up to now, in hard materials, the largest volumes of analysis have been limited to a few hundreds of microns across with nano-meter resolution by using plasma focused ion beam (PFIB). Recently, femto-second (fs) laser based PFIB tri-beam, has become available for 3D serial sectioning technique. In this project we will use one of the first commercial tri-beam systems in the world to radically extend the power of region of interest imaging and correlative microscopy, where alongside three dimensional (3D) structural information it will include 3D local crystal orientation, texture and chemical information to create multi-modal datasets. This will enable mm-sized regions of interest identified in cm sized volumes by X-ray CT to be extracted from materials and devices for higher resolution electron microscopic characterisation to investigate microstructures in high spatial resolution (<100 nm), which is 100 times larger areas and 1000 times larger volumes than other electron microscopy based serial sectioning techniques, today. As an early adopter we need to develop this technology to provide UK researchers with access to unrivalled multi-modal information about new materials systems and devices. This project will contribute to, and all data will be archived in accordance with, Henry Royce Institute Materials 4.0 project. As we develop these methods they will be promoted and made available through Royce to the rest of UK academic and industrial community, accelerating innovation towards net zero and providing the large volume, big data needed to accelerate learning.

Academic background of candidates

B.Sc. with a minimum of 2:i or equivalent, in Material Science or Physics

At the University of Manchester, we pride ourselves on our commitment to fairness, inclusion and respect in everything we do. We welcome applications from people of all backgrounds and identities, and encourage you to bring your whole self to work and study. We will ensure that your application is given full consideration without regard to your race, religion, gender, gender identity or expression, sexual orientation, nationality, disability, age, marital or pregnancy status, or socioeconomic background. All PhD places will be awarded on the basis of merit.

To apply please follow the link below:

https://www.manchester.ac.uk/study/postgraduate-research/admissions/how-to-apply/