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Eingeladener Vortrag
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Multiple timescales in the photoswitching kinetics of crystalline thin films of azobenzene-trimers
(2017)
Functional materials that exhibit photoinduced structural phase transitions are highly interesting for applications in optomechanics and mechanochemistry. It is, however, still not fully understood how photochemical reactions, which are often accompanied by molecular motion, proceed in confined and crystalline environments. Here we show that thin films of azobenzene trimers exhibit high structural order and determine the crystallographic unit cell. We demonstrate that thin film can be switched partially reversibly between a crystalline and an amorphous phase. The time constant of the photoinduced amorphisation as measured with real-time x-ray diffraction ($\approx $ 220 s) lies between the two time constants (120 s and 2870 s) of the ensemble photoisomerisation processes that are measured via optical spectroscopy. Our observation of a photoinduced shrinking of the crystalline domains indicates a cascading process, in which photoisomerisation starts at the surface of the thin film and propagates deeper into the crystalline layer by introducing disorder and generating free volume. This finding is important for the rapidly evolving research field of photoresponsive thin films and smart crystalline materials in general.
Wir zeigen Resultate für einen faseroptischen Temperatursensor, der auf der temperaturabhängigen Eintrübung einer wässrigen Polymerlösung beruht. Da der Sensor auf dem Phasenübergang der spinodalen Entmischung bei fester Temepratur beruht, kann sich der Sensor selbst kalibrieren und daher für Anwendungen zur absoluten Temperaturmessung eingesetzt werden.
Artificial intelligence (AI), machine learning, and neural networks have revolutionized fields such as self-driving cars or machine translation. Indeed, AI has progressed so far that scientists such as Stephen Hawking now fear that it may destroy humankind altogether. So, let’s get started and use AI in surface science.
Here we train neural networks to use raw measurement data as input and immediately return the desired output parameters. We discuss the example of X-ray reflectivity measurements of ultrathin films, which contain reciprocal space information and must traditionally be fitted with dynamic scattering theory. Instead, we train the neural network with simulated X-ray data of multilayer structures and then apply it to measurement data. The neural network yields high accuracy results, is robust against noise, and performs significantly faster than fitting algorithms in real-time experiments. The presented neural network data analysis is becoming increasingly attractive, because free software has become very accessible and specialized computer chips (NPU) are currently being rolled out.
Fiber Optic sensing @BAM
(2017)