Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model.

Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds. These point cloud data are obtained, for example, through the localization of single emitters in super-resolution fluorescence microscopy. External stimuli, such as temperature, can cause changes in the shape and properties of adaptive objects. Due to the demanding and time-consuming nature of super-resolution microscopy experiments, only a limited number of temperature steps can be performed. Therefore, we propose a deep generative model that learns the underlying point distribution of temperature-dependent microgels, enabling the reliable generation of unlimited samples with an arbitrary number of localizations. Our method greatly cuts down the data collection effort across diverse experimental conditions, proving invaluable for soft condensed matter studies.

Controlling microgel deformation via deposition method and surface functionalization of solid supports.

Soft matter at solid-liquid interfaces plays an important role in multiple scientific disciplines as well as in various technological fields. For microgels, representing highly interesting soft matter systems, we demonstrate that the preparation method, i.e. the way how the microgel is applied to the specific surface, plays a key role. Focusing on the three most common sample preparation methods (spin-coating, drop-casting and adsorption from solution), we performed a comparative study of the deformation behavior of microgels at the solid-liquid interface on three different surfaces with varying hydrophilicities. For in situ visualization of the deformation of pNIPMAM microgels, we conducted highly sensitive 3D super resolution fluorescence microscopy methods. We furthermore performed complementary molecular dynamics simulations to determine the driving force responsible for the deformation depending on the surface and the deposition method. The combination of experiments and simulations revealed that the simulated equilibrium structure obtained after simulation of the completely dry microgel after deposition is retained after rehydration and subsequent fluorescent imaging.

Deformation of Microgels at Solid-Liquid Interfaces Visualized in Three-Dimension.

Solid-liquid interfaces play an important role for functional devices. Hence, a detailed understanding of the interaction of soft matter objects with solid supports and of the often concomitant structural deformations is of great importance. We address this topic in a combined experimental and simulation approach. We investigated thermoresponsive poly(N-isopropylmethacrylamide) microgels (µGs) at different surfaces in an aqueous environment. As super-resolution fluorescence imaging method, three-dimensional direct stochastical optical reconstruction microscopy (dSTORM) allowed for visualizing µGs in their three-dimensional (3D) shape, for example, in a "fried-egg" conformation depending on the hydrophilicity of the surface (strength of adsorption). The 3D shape, as defined by point clouds obtained from single-molecule localizations, was analyzed. A new fitting algorithm yielded an isosurface of constant density which defines the deformation of µGs at the different surfaces. The presented methodology quantifies deformation of objects with fuzzy surfaces and allows for comparison of their structures, whereby it is completely independent from the data acquisition method. Finally, the experimental data are complemented with mesoscopic computer simulations in order to (i) rationalize the experimental results and (ii) to track the evolution of the shape with changing surface hydrophilicity; a good correlation of the shapes obtained experimentally and with computer simulations was found.

Perovskite Catalyst for In-Cylinder Coating to Reduce Raw Pollutant Emissions of Internal Combustion Engines.

Aiming to achieve the highest combustion efficiency and less pollutant emission, a catalytic coating for cylinder walls in internal combustion engines was developed and tested under several conditions. The coating consists of a La0.8Sr0.2CoO3 (LSCO) catalyst on an aluminum-based ceramic support. Atomic force microscopy was applied to investigate the surface roughness of the LSCO coating, while in situ diffuse infrared Fourier transform spectroscopy was used to obtain the molecular understanding of adsorption and conversion. In addition, the influence of LSCO-coated substrates on the flame quenching distance was studied in a constant-volume combustion chamber. Investigations conclude that an LSCO coating leads to a reduction of flame quenching at low wall temperatures but a negligible effect at high temperatures. Finally, the influence of LSCO coatings on the in-cylinder wall-near gas composition was investigated using a fast gas sampling methodology with sample durations below 1 ms. Ion molecule reaction mass spectrometry and Fourier transform infrared spectroscopy revealed a significant reduction of hydrocarbons and carbon monoxide when LSCO coating was applied.