Smart Nanomaterial Characterisation

About the project

About the Project This project is part of cohort 4 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute. Electron microscopy is often used in the characterisation of nanomaterials, with scanning electron microscopy (SEM) and transmission electron microscopy (TEM) covering a wide range of nano- and micro-length scales, and associated spectroscopies providing chemical information. The ability to image and undertake analysis (size, structure, composition) at the individual particle level is unmatched by other techniques. One challenge is that electron microscopy is disadvantaged by limited field of view and it being a manual approach. This results in a limited amount of data produced, often with a single “representative” image or data set being used to support characterisation from bulk techniques. A source of bias is introduced as a consequence of it being a manual approach. An aim of this project is to create automated workflows for representative nanomaterial characterisation, encompassing data collection and subsequent data analysis. This research will challenge current approaches to electron microscopy with a view to establishing the potential for it to be a high-throughput technique with reduced bias. The initial focus will be on nanomaterials, as electron microscopy is frequently used for size measurements and the immediate impact of automation approaches will be significant. The effect of different sizes will be assessed in terms of the instrumentation used, SEM compared to TEM, utilising the automation processes developed. The multitude of signals available via electron microscopy within imaging, spectroscopy and diffraction will require the development of a range of automation procedures, analysis workflows and data-centric interpretations. Critically, the sample preparation will be optimised utilising different approaches. By harnessing advanced computer vision and artificial intelligence (AI) techniques, combined with automated cloud-based workflows, large-scale analysis of nanomaterials becomes feasible – enabling the generation of richly annotated datasets for detailed material characterisation. Generative AI can be employed to produce textual summaries of sample scans, allowing researchers to gain high-level insights across broad, representative samples before exploring specific areas of interest. Through the application of sophisticated computer vision algorithms, researchers can rapidly automate statistical analyses, delivering granular metrics on particle size, clustering, composition, and structural properties. This data-driven approach, powered by large-scale, unbiased sample collection, significantly reduces sample bias, improves reproducibility, and supports more robust, scalable insights into nanomaterial properties and behaviour. Nanoparticles have applications in a broad range of applications, and this project will focus on biomedically relevant particles and a further aim is to examine the impact sample preparation has on the representative analysis of these materials. This will encompass the use of cryogenic approaches, enabling analysis of frozen specimens in their native state, which when linked with the automated workflows will open routes to large scale native state analysis of biomedically relevant nanoparticles. The researcher will be trained on the world leading electron microscopy facilities in The Bragg Centre for Materials Research and will be part of the Leeds Electron Microscopy and Spectroscopy (LEMAS) group. Project progress will be accelerated through the researcher being trained in the use of the new instrumentation including the Tescan Amber X Focused Ion Beam Scanning Electron Microscope, equipped with both EDX and TOF-SIMS, and the Tescan Tensor, set up for 4D STEM analysis. These microscopes, and others within the facility are optimised for structural and compositional analysis and using specialist holders can be used to examine frozen samples. Funding Notes This is a fully-funded project, part of cohort 4 of the EPSRC CDT in Developing National Capabilities in Materials 4.0. The studentship covers fees (home), a tax-free stipend of at least £21,805 plus London allowance if applicable, and a research training support grant. Enquiries For general enquiries, please contact doctoral-training@royce.ac.uk . For application-related queries, please contact phd@engineering.leeds.ac.uk . If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Nicole Hondow ( n.hondow@leeds.ac.uk ). Application Process Please note that each partner of the CDT in Materials 4.0 will have its own application process. The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups. Application Web Page https://prod.banner.leeds.ac.uk/ssb/bwskaloguol.PDispLoginNon Select research degree - research postgraduate, then 2027/28 academic year and, under 'planned course of study', choose 'EPSRC CDT Materials 4.0'.

Research areas