Abstract

In the last two decades, advances in the field of transmission electron microscopy (TEM, and its scanning version STEM) have given us capabilities to probe the atomic arrangements, and electronic structures of nanomaterials at an unprecedented level. However, materials property measurements such as optical properties, are at much larger length scale due to the limitations of the source wavelength. As a result, there is scale gap to directly linking the materials properties and their atomic level structures.

In the case of fluorescent nanomaterials, current understanding of their optical properties relies largely on the optical measurement methods from either ensemble of materials or from few single particles. As most of nanomaterial synthesis, in reality, has limited uniformity, characterization of the fluorescent nanomaterial structure-property relationship using such types of optical methods only is insufficient.

To overcome the scale gap, we have developed a new method based on correlative transmission electron microscopy and photoluminescence (TEMPL) which allows a direct correlation of the fluorescence brightness and three-dimensional size and shape of individual nanoparticles. PL provides optical information with exquisite energy resolution, and TEM provides structural information with exquisite spatial resolution. Unsupervised machine learning (ML), using generalized 3D shape descriptors, is used to analyse correlations between the PL brightness and 3D shape of fluorescent particles. Our methodology has been demonstrated on fluorescent nanodiamond particles (FND) containing light-emitting nitrogen vacancy centers (NV). The FND particle size, shape and surface treatment effects have been explored using this new method to reveal previously not known properties.

Lastly, I would also touch upon on our other recent machine-learning based cryo-TEM imaging method to understand the colloidal behaviour of nanodiamond materials in aqueous and biological relevant environment. The understanding of complex aggregation behaviour is currently still lacking but such behaviour is critical in biomedical and catalysis applications.

Biography

Shery Chang is Associate Director of the Electron Microscope Unit at the Mark Wainwright Analytical Centre (MWAC) and Associate Professor in the School of Materials Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. She brings a wealth of international experience, having previously worked in leading electron microscopy research institutes in the USA and Europe, including Arizona State University and the Ernst Ruska-Centre at Forschungszentrum Jülich, Germany. Shery earned her Ph.D. in Materials Science from the University of Cambridge, UK.

Her research combines the power of machine learning with advanced correlative transmission electron microscopy to reveal the atomic scale mechanism in novel fluorescent and ferroic materials. Through this newly developed approach, she is pushing the boundaries of conventional electorn microscopy that enables innovations in sensing, electronics, and quantum technologies.