Scheme: Newton International Fellowships
Organisation: University of Cambridge
Dates: Jan 2014-Jan 2016
Summary: My research is focused on the development and use of sophisticated machine learning algorithms in the characterization of advanced nano-materials. Machine learning algorithms are already used in facial recognition software to efficiently identify and match a subject’s facial features to large image databases. In the materials sciences, machine learning can be applied to multi-dimensional datasets obtained by electron microscopy to identify chemical patterns in the data corresponding to the different phases present in the material. Once identified, the different phases can be reconstructed in three dimensions (3D) to obtain the full structural and chemical composition of the material at the nanoscale. Such information is essential to fully understand structure-property relationships in materials.
I plan to use the technique initially to study (1) nickel-based superalloys, (2) lithium-ion batteries and (3) fluorescent core-shell biomarkers.
(1) Mapping the 3D distribution of trace elements in nickel-based superalloys used in turbine engines and relating their distribution to the underlying microstructure will potentially improve our current understanding of their superior mechanical strength at high temperatures.
(2) The 3D structural and valence state modifications of nano-composite lithium-ion electrodes during discharge-recharge reactions can be studied, potentially providing precious insight into the exact mechanisms of lithium insertion, key to improving battery performance.
(3) Information about the nanoscale morphology of fluorescent core-shell biomarkers via advanced microscopy may provide key information for the improved synthesis and optimization for in-vivo cell labelling and targeted bio-imaging.