Lecture Hall HCI J7, 16:30
Prof. Arkadiy Simonov
Laboratory for Disordered Materials, ETH Zürich
Gas Storage in Disordered Materials
Gas storage materials are typically studied through average structure measurements, but this approach misses important structural changes during material activation and gas cycling. Using diffuse X-ray scattering on Prussian Blue materials – frameworks previously considered stable and straightforward for hydrogen storage – we reveal hidden structural transitions that occur during gas storage processes. Dehydration causes a significant reorganization of the local structure, creating new transport pathways through the material, while further structural modifications occur during gas absorption and desorption cycles. These changes, which remain invisible to average structure measurements yet significantly affect storage performance, demonstrate the importance of examining local structure during gas storage processes for developing better storage materials.
Dr. Quinn Besford
Leibniz Institute of Polymer Research Dresden
Leveraging Conformationally-Fluorescent Polymers for In Situ Sensing of Interfacial Phenomena
Spatially resolving interfacial stimuli can allow us to better understand physicochemical processes. Towards this goal, we develop ways of integrating Förster resonance energy transfer (FRET) chemistry into stimuli-responsive polymer architectures, such that FRET output (i.e., spectral shift in output fluorescence) reflects polymer chain conformation. Using FRET, we can spatially resolve conformation by confocal microscopy methods, with high spatiotemporal resolution. In this talk, I will discuss strategies to integrate FRET into functional polymers, and how we can examine the responsiveness of the polymers for understanding physicochemical processes. Specifically, these systems are used to study surface wettability, localized changes in pH, resolving ion fluxes, and micro-to-nano structure in secondary matrices. Our systems provide a non-invasive optical measure of changing surface conditions, which holds great promise for identifying processes in real-time.
