Welcome to the Electron Imaging for Advanced Materials Group! Here in the EIAM Group we aim to fully expound the potential plethora of information on advanced materials which can be made available via the superb spatial resolution of electron microscopy. Our research comprises two main streams: one the one hand, the development of new state-of-the-art electron microscopy techniques, and, on the other hand, targeted applications of such techniques to discover structure-properties of key advanced materials (see list below). We are privileged to collaborate closely with fantastically dedicated and experienced materials scientists and companies from across the globe. Our research is truly multidisciplinary in nature, incorporating scientific expertise which includes materials fabrication and synthesis, experimental aberration-corrected electron microscopy and spectroscopy, materials modelling, and advanced image and data processing methods such as machine learning and computer vision.

Interested in working with us? Contact A/Prof Shery Chang for further information.

Research Programs

Discovering materials properties by machine learning of big data electron microscopy

Electron microscopy offers exquisite spatial resolution in imaging and spectroscopy, and is generally applicable to an enormous range of materials, making it extremely powerful for the discovery of structure-properties relationships. On the other hand, a common criticism of electron microscopy is that the small field of view at high spatial resolution can lead to poor statistics and the possible misrepresentation of a given sample. To overcome this limitation, the EIAM Group is developing approaches utilising machine learning of big data sets. In this approach, automation is used to acquire large amounts of electron imaging and spectroscopy data which is fully representative of a given sample. Depending on the application, a range of machine learning algorithms, from unsupervised learning to CNN deep learning algorithms, with inspiration drawn from the fields of computer vision and object recognition, are applied to produce data classifications representative of materials properties, e.g., opto-electronic properties or electronic structure. This methodology represents a paradigm shift in electron microscopy technique, enabling critical insight into a variety of new advanced materials.

Electron imaging diagram


Nanodiamond for sensing and biomedical applications

Nanodiamond has been demonstrated to possess the exciting properties of tuneable surface chemistry and non-toxicity. In the EIAM Group, we are interested in engineering and understanding nanodiamond surfaces and defects at the atomic level for sensing applications. In addition, very small nanodiamond (< 5 nm) has been shown to be an excellent carrier of drug molecules, proteins and DNA for biomedical applications. We are interested in understanding and controlling the interactions between nanodiamonds and the “soft” biological molecules as well as their interaction with the liquid environment. We primarily cryo-TEM methods, as well as other spectroscopy methods to investigate these properties.

Electron imaging diagram


Single photon emitters in diamond and 2D materials

Colour centres in diamond, such as nitrogen- and silicon-vacancy centres, are known to emit single photons with high efficiency, even at the room temperature in the case of NV centres. The recent discovery of single photon emission in 2D materials has elevated its status to emerging candidates for quantum sensing and quantum information technologies. In the EIAM Group, we apply electron beam imaging and spectroscopy to understand the configurations of atomic defects responsible for single photon emission, as well understanding how their photonic/optical properties are influenced by the surrounding structural and chemical environment of the host.

Electron imaging diagram


Group Leader

Group Members

Inga Kuschnerus (research fellow)

Inga Kuschnerus



Haotian Wen (PhD student)

Haotian Wen



Bo Wu (Masters student)

Bowen Wang (Masters student)

Yang Tao (Masters student)


Zichen Xu (Honours student)