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Synchrotron X-ray chemical tomography methods combine a scattering or spectroscopic technique with a tomographic data acquisition approach. These non-destructive methods yield a cross-section of the studied sample where each pixel in the reconstructed images corresponds to a chemical signal (e.g. X-ray diffraction pattern or spectrum). These spatially-resolved signals most often reveal information that is lost in conventional bulk measurements. In this talk, I will briefly introduce X-ray diffraction computed tomography (XRD-CT) and present a few examples where we have applied such methods to track the evolving solid-state chemistry of complex functional materials and devices under operating conditions. In the second part of the webinar, I will focus on our latest technical advances regarding processing and analysis of these large and rich chemical imaging datasets using deep learning methods with neural networks.

Bio:
Dr Antony Vamvakeros is the Research and Development Lead Scientist at Finden Ltd, working on the development of new algorithms and machine learning approaches for analysing big scientific data. His research focuses on advancing and applying chemical imaging techniques to study solid heterogeneous catalysts, fuel cells and batteries under operating conditions. Antony graduated with a Diploma in Chemical Engineering from University of Patras before moving to the United Kingdom where he obtained an MSc in Chemical Process Engineering and a PhD in Chemistry from University College London. He then spent two years at the ESRF working as an industrial post-doctoral scientist at beamline ID15A.