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| Reference Number | EP/X011941/1 | |
| Title | Hexagonal Perovskite Derivatives for Next-Generation Ceramic Fuel Cells | |
| Status | Started | |
| Energy Categories | Hydrogen and Fuel Cells (Fuel Cells) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 20%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 20%; ENGINEERING AND TECHNOLOGY (Chemical Engineering) 40%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor AC Mclaughlin Chemistry University of Aberdeen |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 April 2023 | |
| End Date | 31 March 2026 | |
| Duration | 36 months | |
| Total Grant Value | £374,750 | |
| Industrial Sectors | R&D | |
| Region | Scotland | |
| Programme | NC : Physical Sciences | |
| Investigators | Principal Investigator | Professor AC Mclaughlin , Chemistry, University of Aberdeen (100.000%) |
| Web Site | ||
| Objectives | ||
| Abstract | To combat climate change, it is predicted that one third of the UK's energy consumption must be hydrogen-based in order to achieve net zero emissions by 2050. Fuel cells will play a key role in achieving this ambitious target. In a fuel cell the chemical reaction between hydrogen and oxygen produces water and electricity, providing a clean alternative to fossil fuels. Ceramic fuel cells are highly efficient, don't require ultra-pure hydrogen and are also 'fuel flexible', meaning that hydrocarbons such as natural gas can also be used as a fuel, but with much lower greenhouse gas emissions compared to petrol or diesel. The ceramic fuel cell can therefore also act as a bridging technology as we move away from fossil fuels.To simplify issues such as sealing, poor lifetime and to enable the use of cheaper steel interconnects, the ceramic fuel cell's operating temperature needs to be reduced from 800 degrees C to an intermediate range of 400-600 degrees C. To reach this goal, new materials that exhibit oxide ion/proton conductivity greater than 10 mS cm-1 at intermediate temperature are needed for the next generation of ceramic hydrogen fuel cells. Such fuel cells will be more cost-effective and have greater longevity and hence will be more economical for replacing fossil fuels for zero-carbon energy generation.Our recent discovery of high oxide ion and proton conductivity at 500 degrees C in the hexagonal perovskite derivative Ba7Nb4MoO20 is ground-breaking as the conductivity is competitive with state-of-the-art materials and it is yet to be tuned. A further advantage of Ba7Nb4MoO20 over state-of-the-art oxide ion and proton conductors is that it is both easy to process and it is highly stable under CO2, H2O and reducing atmospheres. We propose to perform targeted chemical doping and processing studies to further enhance the dual ion conductivity of Ba7Nb4MoO20. This important fundamental research will be transformative as improved materials will enable a step change in the performance of ceramic fuel cells. We will also design and explore the conductivity of further hexagonal perovskite derivatives to build up structure property relationships in this important new family of ionic conductors. These materials are highly complex, and we will use a combined experimental and computational modelling approach to both direct target materials and unravel structure-property relationships in this new class of electrolyte materials. In order to realise the true potential of oxide ion/proton conducting hexagonal perovskites and enable future material design, an in-depth atomistic understanding of the underlying ion transport mechanisms is essential. To gain a comprehensive understanding of the mechanisms of oxide and proton conductivity at both the bulk and microstructural scales in these complex materials, we will also perform computational studies on ion diffusion and defect mechanisms using a powerful combination of molecular dynamics (MD), ab initio (DFT ) and machine learning techniques | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 10/05/23 | |
