Representative chemistry and strain analysis of fuel cell catalyst nanoparticles via machine learning and DFT modelling

通过机器学习和 DFT 建模对燃料电池催化剂纳米颗粒进行代表性化学和应变分析

• 批准号: 2734027
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2734027
• 关键词: Representative; chemistry; strain; analysis; fuel

The use of hydrogen is increasingly seen as a key component to meeting our goals on net zero carbon energy sources. Hydrogen fuel cells are used to convert hydrogen to electricity, but are limited by the sluggish oxygen reduction reaction at the fuel cell cathode. Catalysts based on platinum are used to increase this reaction rate, but Pt is an expensive metal limiting the economic viability of hydrogen-based energy. Alloying Pt with cheaper base metals reduces the mass of Pt required, and surprisingly can enhance activity beyond that of Pt. The origins of the enhanced activity are not fully understood and may be associated with compositional clustering of species, the chemical effects of mixing metals or the effect of lattice strain if the composition is inhomogeneous. The challenge is that measuring either strain or composition within a nanoparticle is right at the limits of current experimental capabilities, especially as we need methods that can examine many particles to understand the ensemble properties. This project will make use of state-of-the-art electron microscope technologies for imaging and spectroscopy to determine composition including degree of oxidation and the resulting strain. Scanning Transmission Electron Microscopy (STEM) (a technique in which there has been substantial investment in the UK) will be the primary experimental tool. STEM will be used to form atomic-resolution images and to simultaneous measure composition using electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray (EDX) spectroscopy. Machine learning will be used to allow a larger numbers of particles to be analysed. Previous work has shown that high levels of shear strain are present, but the effect of this on the electronic structure of the catalysts has not been studied. Density functional theory modelling will be used to understand the link between structure and activity, based on the structure, strain and composition that is measured using the electron microscope studies. Developing a full understanding of the link between structure and activity is an important step in the development of new catalyst systems.This project aligns very closely with the EPSRC Energy theme, which states as a priority "The overarching goal of the Energy theme is to sponsor research and PhD training to secure a low-carbon future, through the creation of reliable, economically viable energy systems while protecting the natural environment, resources and quality of life." The methodological aspects of the work will also support other activities within the more general Physical Sciences theme.

氢的使用越来越被视为实现我们净零碳能源目标的关键组成部分。氢燃料电池用于将氢转化为电，但受到燃料电池阴极处缓慢的氧还原反应的限制。基于铂的催化剂用于提高该反应速率，但是Pt是昂贵的金属，限制了基于氢的能量的经济可行性。将Pt与更便宜的贱金属合金化减少了所需Pt的质量，并且令人惊讶地可以增强超过Pt的活性。增强的活性的起源尚未完全了解，可能与组成群集的物种，混合金属的化学效应或晶格应变的影响，如果组合物是不均匀的。挑战在于，测量纳米颗粒内的应变或成分正处于当前实验能力的极限，特别是当我们需要可以检查许多颗粒以了解整体性质的方法时。该项目将利用最先进的电子显微镜技术进行成像和光谱分析，以确定成分，包括氧化程度和产生的应变。扫描透射电子显微镜（STEM）（在英国有大量投资的技术）将是主要的实验工具。STEM将用于形成原子分辨率图像，并使用电子能量损失谱（EELS）和能量色散X射线（EDX）谱同时测量成分。机器学习将用于分析更多数量的粒子。以前的工作表明，存在高水平的剪切应变，但这对催化剂的电子结构的影响尚未研究。密度泛函理论建模将用于了解结构和活动之间的联系，基于使用电子显微镜研究测量的结构，应变和组成。充分理解结构和活性之间的联系是开发新催化剂系统的重要一步。该项目与EPSRC能源主题非常一致，该主题将其列为优先事项：“能源主题的总体目标是通过创建可靠的，经济上可行的能源系统，同时保护自然环境，资源和生活质量。“这项工作的方法方面也将支持更一般的自然科学主题范围内的其他活动。