6 Materialchemie
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By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
This presentation highlights ongoing scientific misconduct as found in academic literature. This includes data- and image manipulation, and paper mills. Starting with an expose of examples, it delves deeper into the causes and metrics driving this phenomenon. Finally a range of possible tools is presented, that the young researcher can use to prevent themselves from sliding into the dark scientific methods.
Interactions between a polymer and a substrate interface play a vital role in understanding the improvement in thin film material properties as well as serving as a model for nanocomposites. For any non-repulsive polymer-substrate interactions, polymer segments form an irreversibly adsorbed layer and show a slowdown in the glassy dynamics and thus an increase in the thermal glass transition temperature compared to the bulk-like values. The growth kinetics of the adsorbed layer showed a deviation for both poly (bisphenol-A carbonate) (PBAC) and polysulfone (PSU), two bulky polymers containing a functional group (phenyl ring) in the backbone, compared to conventional polymers previously studied like poly-2-vinyl pyridine (P2VP). This deviation was attributed to the bulkiness of the phenyl rings. . Further investigations into the influence of the adsorbed layer on glassy dynamics were conducted. The molecular mobility and glass transition for thin films of PBAC and PSU were compared to bulk samples of each polymer. Broadband dielectric spectroscopy, atomic force microscopy, and ellipsometry were primarily used and additionally supported by sum frequency generation spectroscopy.
New analytic ways to characterise mesoporous thin layers used in electrocatalytic water splitting
(2024)
Mesoporous materials are needed in many applications where a high specific surface area and adsorptive behaviour is needed. Important examples are separation techniques and barrier layers and catalysts. Electrochemical water splitting is the key technology for producing green hydrogen and there is no foreseeable alternative to this process for producing elementary hydrogen from green electrical power. Water electrolysis can be divided into the anodic Oxygen Evolution Reaction (OER) and the cathodic Hydrogen Evolution Reaction (HER). Both processes have to be heavily optimised to a large extent to avoid energy losses caused by overvoltage. The development of electrodes for these processes is especially difficult due to the many boundary conditions. Water splitting is a catalytic as well as electrochemical process. The contact area between the electrolyte and the electrode must be maximised maintaining the stability of the surface. Side reactions must be suppressed, and effective gas transport must be ensured. The whole process has to be tolerant with respect to temperature, harsh chemical conditions from the electrolyte as well as high current densities.
We present a hybrid analytical method combining several analytical techniques for determining the properties of thin layers of mixed oxides of the general composition Ir:TiOx. These materials are promising candidates for electrocatalytical top coatings of OER electrodes. To lower the costs of the electrolysers, the main goal is to lower the Ir content retaining the system efficiency. The main properties which are hard to determine are the porous volume fraction and the Ir:Ti element ratio. By a combination of electron microscopy, spectroscopic operando ellipsometry, ellipsometric porosimetry, and other techniques, we can determine key features of mesoporous thin layer materials. We aim to develop operando capable techniques used in process monitoring as well as measurement techniques optimised for accuracy. By developing reference materials, we support long term uptake of our methodology. This work can directly be used for optimising electrocatalytic layers and is a good example for the power of hybrid metrology for improving materials design.
Polydopamine (PDA) is a biological / biomimetic polymer which has spiked considerable interest in recent years. Its monomer is an important neurotransmitter and it is one of the strongest glues produced by biological organisms. Polydopamine is a candidate for several applications, mainly in the field of biology and medicine, but also - recently - for layer coatings with optical, electrical, and mechanical function.
In this work, we investigate PDA layers intended as reflectivity enhancers for mirror surfaces in X-ray astronomical observatories. It has previously been shown, that such X-ray telescopes can be improved by a coating of PDA in the thickness range of several nm. Accurate thickness determination is required to monitor and optimise the coating process. We use spectroscopic ellipsometry to determine first the dielectric function of the polydopamine layers using model coatings of sufficient thickness. This data is then used to accurately determine the layer thickness of much thinner PDA layers. This study resulted in data on the thickness and dielectric function of PDA layers that could lead to a better understanding of the correlation of layer thickness and layer properties depending on the process parameters.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.