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

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

CoPED ID
488222f7-20f0-4eab-bcdc-c658f55901e1

Status
Active

Funder

Value
No funds listed.

Start Date
Sept. 30, 2022

End Date
March 30, 2026

Description

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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.

University of Oxford LEAD_ORG
Johnson Matthey Plc STUDENT_PP_ORG

Peter Nellist SUPER_PER
Alessandro Zanre STUDENT_PER

Subjects by relevance
  1. Hydrogen
  2. Machine learning
  3. Energy
  4. Electron microscopy
  5. Fuel cells
  6. Spectroscopy

Extracted key phrases
  1. Fuel cell catalyst nanoparticle
  2. Hydrogen fuel cell
  3. Representative chemistry
  4. Fuel cell cathode
  5. Lattice strain
  6. Shear strain
  7. New catalyst system
  8. Use
  9. Carbon energy source
  10. Simultaneous measure composition
  11. Cheap base metal
  12. Machine learning
  13. Electron energy
  14. Viable energy system
  15. Sluggish oxygen reduction reaction

Related Pages

UKRI project entry

UK Project Locations