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Dr Nicole Hondow

Supervisor

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.

Dr Josh Owens

Supervisor

Additive Manufacturing (AM) offers major advantages in design flexibility and material efficiency, but surface texture remains a key barrier to wider adoption, especially for corrosion-critical applications. This project in collaboration with Holdson, specialists in sustainable electropolishing for aerospace, medical, and renewable energy sectors, aims to develop and optimise a novel electropolishing process tailored for AM metal alloys to minimise corrosion.

The initial phase of the project will evaluate corrosion performance of stainless steel produced via Laser Powder Bed Fusion, comparing various surface treatment approaches. Corrosion testing will replicate harsh conditions relevant to Holdson’s applications. A core innovation of this project is the development of high-throughput corrosion screening methods, enabled by multi-material components produced through advanced AM techniques. This approach enables batch electropolishing and rapid electrochemical testing at specific locations, correlating local alloy composition and microstructure with corrosion behaviour. Advanced microscopy will provide detailed surface and material analysis. Finally, the project will use high-throughput data to build a machine learning framework that predicts optimal electropolishing parameters for AM metal alloys, to support industrial implementation by Holdson.

Dr James McHugh

Supervisor

Interfacial ferroelectrics are an emerging class of ferroelectric (FE) materials formed by introducing symmetry-breaking interfaces between stacked 2D van der Waals (vdW) crystals. This architecture enables polarisation without conventional lattice distortions: the out-of-plane component is set by stacking and can be reversibly switched through in-plane sliding. Because the sheets interact via weak vdW forces, these heterostructures are resilient to charge trapping, mechanical strain, and long-term degradation. Stacking multiple layers permits multi-level, dynamically switchable polarisation states, enabling richer emergent behaviour and precise control of functionality. This out-of-plane FE can be exploited in field-tuneable devices: band-alignment engineering for reconfigurable optoelectronics, ferroelectric tunnel junctions (fTJs) as analogue synapses, and two-dimensional multiferroics realised by stacking monolayer magnets.

The design space spans many “interface architectures,” dictated by layer chemistry, stacking sequence, and interfacial twist at each boundary in a multilayer stack. Understanding the atomic-level interplay of FE and structure across this space is the key barrier to reproducible, high-precision realisation of targeted functionality. Switching often involves dislocation-mediated motion and twist-dependent relaxation; together with mixed chemistries, this demands tightly coupled theory and large-scale computation.

This project will investigate multilayer functional devices built from stacked monolayer semiconductors and magnetic materials. High-throughput ab-initio Density Functional Theory (DFT) will provide layer-resolved properties—band edges, out-of-plane dipoles and magnetic moments—as electrically tuneable order parameters. Lightweight Atomic Cluster Expansion (ACE) machine-learning interatomic potentials (MLIPs) will then efficiently scan chemistry/stacking/twist space, identifying structures with strong out-of-plane FE responses suited to sliding fTJs, field-tuneable optoelectronics, and low-power multiferroic spin-valves and memories. Finally, the student will develop multi-modal, charge-aware MLIPs, trained on the DFT corpus, to predict energies, forces and local charge densities, quantifying how point defects and impurities influence collective polarisation and switching pathways. In the longer term, such models could aid studies of nano-catalysis in disordered environments and improve orbital-free DFT through better kinetic-energy approximations.

Prof Ping Xiao

Supervisor

This PhD project will accelerate the development of novel environmental barrier coatings required for ceramic matrix composite components for the next generation of higher temperature aero-engines in collaboration with Rolls-Royce through machine learning. Silicon carbide-based ceramic matrix composites (SiC-SiC CMCs) are being developed to transform aeroengine design by replacing nickel-based superalloys in the hot section of an aero-engine, due to their superior high-temperature mechanical properties and lower density e.g. density of CMCs is about one third density of superalloys. The use of the lighter weight CMCs would allow engine operating at higher temperature, using less cooling air, and therefore fuel. However, environmental barrier coatings (EBCs) are required to protect SiC-SiC CMCs from steam corrosion in engine environment. Without EBCs, the underlying substrates rapidly degrade in the hostile gas-turbine environment, so the failure of the EBCs is life limiting for the entire component. To introduce EBCs into aero-engine service, extensive research on manufacture, characterization and testing of EBCs has been carried out over decades. A range of materials have been investigated, but the current state-of-the art EBCs are based on ytterbium disilicate deposited via the air plasma spraying (APS) process.

Prof David Fletcher

Supervisor

• The student will execute the foundational FAST-TRACK cycle: Explore compositional space using CALPHAD and simple ML models. Manufacture a materials library and begin the high-throughput characterisation pipeline. Complete characterisation and perform data analysis to identify structure-property relationships and down-select candidates. Conduct rapid manufacturing assessment, involving powder atomisation and consolidation via LPBF, HIP, and FAST. Additionally, dedicated in-depth analysis of this dataset, presenting initial findings, and planning the second cycle will be carried out.

• A second, more ambitious cycle will be executed. This will involve a more complex alloy system as well as refining and automating the characterisation protocols for even faster screening. A key focus will be integrating machine learning (ML) models, trained on the Year 1 dataset, to predict promising compositions, making Cycle 2 more targeted and efficient.

• The student will run a third cycle, applying the now-refined methodology to address a specific, complex industrial challenge posed by Rolls-Royce. This year will also involve deeper scientific investigation into the fundamental structure-property relationships for Ni-based superalloys at a much broader scale, with manufacturing integrated on a fundamental level thus moving beyond rapid screening to generate new understanding.

• The final year will be dedicated to completing all experimental work, final data analysis, and writing the PhD thesis. A critical component of this project is its commitment to the Materials 4.0 ethos. All new data analysis protocols, computational models, and the validated methodology will be prepared for open access publication. This ensures the final report and data packages provide a robust, transferable blueprint for applying this agile approach to other alloy systems, accelerating knowledge creation and solidifying the UK’s position as a leader in industrial innovation.

Dr Oriol Gavalda Diaz

Supervisor

The long-term performance of energy generation (e.g., nuclear plants) and transport applications (e.g., aviation) is underpinned by ceramic-based coatings and composites that can withstand demanding environments. For instance, thermal barrier coatings (TBCs), environmental barrier coatings (EBCs), and ceramic matrix composites (CMCs) are examples of ceramics which can continue to improve product performance and promote sustainability in the aero-engine industry. Given the harsh conditions, these materials may experience degradation through-life driven by complex combinations of thermal, mechanical, and chemical processes, often accelerated by contaminants such as CMAS (calcium–magnesium–alumino–silicates).

Rolls-Royce has identified Raman spectroscopy as a promising technology for non-destructive and potentially on-wing (without dismounting the aero-engine) inspection of such materials. However, the Raman spectra collected in-service are highly complex, and their interpretation requires new approaches that integrate databases of lab-based characterisation data and data-driven analysis protocols. The key challenge is linking spectral features to specific degradation mechanisms and relate these to the environmental degradation of the component. Moreover, a holistic view of the degradation of the material will require to link these degradation mechanisms to other sources of inspection data (e.g. advanced microscopy techniques or visual inspection).

This project will create a data-rich experimental and analytical framework to overcome these challenges. It will combine the following:

• Controlled laboratory experiments that simulate degradation mechanisms in ceramics, generating reference datasets of Raman spectra across different conditions (temperature, stresses, and contaminant exposure).
• Complementary nanoscale characterisation data (e.g., electron microscopy) to validate the key spectral signatures.
• Advanced data-analysis protocols to link lab data to real applications. Methods such as spectral decomposition, machine learning, and database-driven comparison will be applied to extract characteristic features from the Raman datasets. The goal is to create a systematic approach for data-informed inspection, in which spectra are not simply recorded but actively interpreted against a material database.

Rolls-Royce will provide real aero-engine samples to validate our laboratory-generated databases and analysis protocols. Hence, the outputs of the project will directly support the implementation of in-situ/on-wing inspection tools by Rolls-Royce, reducing reliance on destructive testing, enabling predictive maintenance strategies and extending the lifetime of components.

Dr Abigail Ackerman

Supervisor

​Low temperature electrolysis is an emerging technology that has the potential to revolutionise the ironmaking industry. Iron and steel production currently accounts for around 8% of global CO₂ emissions, with approximately 70% of these emissions resulting from the reduction of iron ore (naturally found as Fe₂O₃). The dominant reduction method—carbon-based blast furnace processing at ~2100 °C—releases oxygen from the ore, which bonds with carbon to form CO₂. On average, producing one tonne of iron this way emits about 1.8 tonnes of CO₂.

​​Low-temperature electrolysis offers a promising alternative. Over the past decade, this experimental method has shown potential to significantly lower both carbon emissions and energy demand compared with other green technologies such as Direct Reduced Iron (DRI), which uses hydrogen as a reducing agent. Unlike many existing approaches, this method produces only oxygen as a by-product and does not require high-purity ore. This makes it particularly suitable for processing lower-grade iron ores and recovering iron from waste streams such as mine water and tailings.

​An important yet underexplored aspect is the application of digital twins to remote sensing of electrolyser cells. In multi-electrolyser systems, digital twins are critical for balancing power input across cells and for predicting component degradation under demanding operating conditions. This project will build on existing digital twin blueprints for electrolysers by developing a robust sensor system that feeds real-time data into a digital twin, designed and implemented by the student. This capability will be central to ongoing upscaling efforts within both the supervisor’s research group and the project’s industrial sponsor.