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Hexagonal Perovskite Derivatives for Next-Generation Ceramic Fuel Cells

Hexagonal Perovskite Derivatives for Next-Generation Ceramic Fuel Cells
用于下一代陶瓷燃料电池的六方钙钛矿衍生物
批准号:
EP/X011941/1
负责人:
Abbie Mclaughlin
金额:
$47.75万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
翻译
为了应对气候变化,预计英国三分之一的能源消费必须以氢气为基础,才能在2050年实现净零排放。燃料电池将在实现这一雄心勃勃的目标方面发挥关键作用。在燃料电池中,氢和氧之间的化学反应产生水和电,为化石燃料提供了一种清洁的替代品。陶瓷燃料电池效率高,不需要超纯氢,而且燃料也很灵活,这意味着天然气等碳氢化合物也可以用作燃料,但与汽油或柴油相比,温室气体排放量要低得多。因此,随着我们远离化石燃料,陶瓷燃料电池也可以作为一种桥梁技术。为了简化密封、寿命差等问题,并能够使用更便宜的钢互连,陶瓷燃料电池的工作温度需要从800摄氏度降低到400-600摄氏度的中间范围。为了实现这一目标,下一代陶瓷氢燃料电池需要在中温表现出大于10ms cm-1的氧化物离子/质子电导率的新材料。这种燃料电池将更具成本效益,寿命更长,因此将更经济地取代化石燃料,以产生零碳能源。我们最近在六方钙钛矿衍生物Ba7Nb4MoO20中发现了500摄氏度的高氧化物离子和质子导电性,这是突破性的,因为其导电性与最先进的材料竞争,还有待调整。与最先进的氧化物离子和质子导体相比,Ba7Nb4MoO20的另一个优势是它易于加工,在CO2、H2O和还原气氛下高度稳定。我们建议进行有针对性的化学掺杂和工艺研究,以进一步提高Ba7Nb4MoO20的双离子导电性。这一重要的基础研究将是变革性的,因为改进的材料将使陶瓷燃料电池的性能发生阶段性变化。我们还将设计和探索进一步的六方钙钛矿衍生物的导电性,以建立这一重要的新离子导体家族的结构和性质关系。这些材料非常复杂,我们将使用实验和计算相结合的建模方法来直接针对目标材料,并揭示这类新的电解质材料的结构-性能关系。为了实现氧离子/质子导电六方钙钛矿的真正潜力,并使未来的材料设计成为可能,深入了解潜在的离子传输机制是必不可少的。为了全面了解这些复杂材料中氧化物和质子的电导机制,我们还将利用分子动力学(MD)、从头算(DFT)和机器学习技术的强大组合,对离子扩散和缺陷机制进行计算研究。
英文摘要
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.
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