About the Project
This project is part of cohort 3 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.
The 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.
The 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.
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.