Scientists at the University of Manchester have used Nobel Prize-winning techniques on metal nanoparticles to reduce the costs of fuel cells and to reduce emissions.
The 2017 Nobel Prize was awarded to Joachim Frank, Richard Henderson and Jacques Dubochet for their role in pioneering the technique of ‘single particle reconstruction’ in nanoparticles. The technique was used to reveal the structure of viruses and was applied to metals, and now, this research has been used to study fuel cells. Fuel cells are cells that work by the transferring of electrons from anode to cathode under certain conditions. From portable power chargers to automobiles, fuel cells are omnipresent in energy-driven industries and processes.
In collaboration with the University of Oxford and Macquarie University, researchers applied atomic-scale chemistry on nanoparticles in a bid to increase their efficiencies. The problem is that prior to this technology, there was no complex imaging that could be done to identify the structure of their fuel cells, the best being rudimentary 2D imaging techniques.
Professor Sarah Haigh, based in the School of Materials, said that they were previously investigating the use of tomography in the electron microscope to map elemental distributions in three dimensions for some time by rotating the particles and taking images from all directions, like a CT scan in a hospital, but these particles were too damaging to enable a 3D image to be built up.
Biologists use a different approach for 3D imaging, so the researchers decided to explore whether this could be used together with spectroscopic techniques to map the different elements inside the nanoparticles. Like ‘single particle reconstruction,’ the technique works by imaging many particles and by assuming that they are all identical in structure but arranged at different orientations relative to the electron beam. The images are then fed into a computer algorithm which outputs a 3D reconstruction.
For better imaging, 3D imaging had to be done to investigate platinum-nickel (Pt-Ni) metal nanoparticles. Lead author, Yi-Chi Wang, from the School of Materials, also said that the 3D local chemical distribution could help researchers design better catalysts that are low-cost and high efficiency. The aim of the school was to make the 3D chemical reconstruction automated to create a fast and reliable method of imaging nanoparticle populations for applications in biomedical sensing, light emitting diodes, and solar cells.
Technological advancements in nanoparticle research have lead to exciting scientific breakthroughs, and in the future, nanoparticles are likely to have profound and diverse applications in all fields of science.