The main scientific driver of our group is the understanding, controlling, and exploiting of the exciting physical phenomena arising in quantum materials such as two-dimensional (van der Waals) nanomaterials and assessing their potential for applications from optoelectronics to nanophotonics.
Machine learning for EELS data interpretation. Data analysis in EELS is powered by a dedicated Python machine learning framework, EELSfitter, developed in-house within our group. It makes it possible to access key properties of 2D nanomaterials such as modulation of the bandgap, dielectric function, dispersion relations, low-energy electronic states, excitons, and other collective excitations, and their direct correlation with structural features. This effort is carried out in collaboration with experts from Nikhef, the Dutch national institute for particle physics. Learn more about our nanofabrication research here.
From quantum materials to quantum technologies. Engineering the resulting functionalities of quantum nanomaterials has an immense potential to revolutionize both the way we think about fundamental material science as well as for technological applications in fields from nanoelectronics and nanophotonics to quantum communication and sensing. For example, with van der Waals materials one could design materials with a strong nonlinear optical response for nanophotonics, drive clean energy reactions by exploiting their unique surface properties, assemble solid-state quantum bits for quantum computers, implement new platforms for ultra-precise quantum sensors, and implement single-photon emitters for secure quantum communications.