Abstract & Bio: https://reurl.cc/2lzAA9
Recent advances in transmission electron microscopy (TEM) allow atomic-scale visualization of materials, yet directly linking atomic structures to functional properties such as optical emission remains challenging, due to the inherent scale gap between structural and property measurements.
This seminar will showcase how correlative and in-situ electron microscopy, combined with machine learning, can bridge this gap in fluorescent and quantum materials.
I will highlight three correlative microscopy studies that my group have recently been investigating. Firstly, a correlative TEM–photoluminescence (TEMPL) method has recently introduced to connect fluorescence brightness with nanoscale morphology and surface chemistry in fluorescent nanodiamonds, materials that have been demonstrated as nanoscale quantum sensors.
Secondly, ferroelastic bismuth vanadate (BiVO₄), excellent candidate materials as photo catalysts were studies using correlative high-resolution STEM and scanning probe microscopy. We showed atomic scale domain-wall structures that are responsible for enhanced conductivity which may improve photocatalytic performance.
Lastly, I will highlight our recent work on in-situ and correlative electron microscopy investigations of lead-free copper halide perovskites to elucidate their structural stability, degradation mechanisms, and electron-beam tolerances. These insights are critical for the design of environmentally stable, high-performance optoelectronic materials.
Together, these approaches demonstrate how correlative microscopy and machine learning driven analysis can unify structure–property understanding, accelerating the design of advanced materials for quantum, catalytic, and optoelectronic applications.