Prof. Dr. Tobias Kraus

Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li-ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.

The significant demand for energy storage systems has spurred innovative designs and extensive research on lithium-ion batteries (LIBs). To that end, an in-depth examination of utilized materials and relevant methods in conjunction with comparing electrochemical mechanisms is required. Lithium titanate (LTO) anode materials have received substantial interest in high-performance LIBs for numerous applications. Nevertheless, LTO is limited due to capacity fading at high rates, especially in the extended potential range of 0.01–3.00 V versus Li+/Li, while delivering the theoretical capacity of 293 mAh g−1. This study demonstrates how the performance of the LTO anode can be improved by modifying the manufacturing process. Altering the dry and wet mixing duration and speeds throughout the manufacturing process leads to differences in particle sizes and homogeneity of dispersion and structure. The optimized anode at 5 A g−1 (≈17C) and 10 A g−1 (≈34C) yielded 188 and 153 mAh g−1 and retained 73% and 68% of their initial capacity after 1000 cycles, respectively. The following findings offer valuable information regarding the empirical modifications required during electrode fabrication. Additionally, it sheds light on the potential to produce efficient anodes using commercial LTO powder.

Batteries contain combinations of materials that undergo electrochemical reactions to convert chemical into electrical energy. Battery research relies on experience and know-how. Important materials and processing data can get overlooked, remain undocumented, or even lost. To bridge the gap between fundamental materials research and battery process engineering, it is essential to generate, analyze, and, most importantly, link intermediate knowledge for future use. Here, it is shown how to combine domain knowledge and a data-driven approach to understanding material–property relationships in the case of conductivity networks of carbon black. The Battery Production and Characterisation Ontology (BPCO) is employed to identify hypotheses that connect battery processing to material domain knowledge. The material's interactions between carbon black, polyvinylidene flouride, and solvents in the BPCO are characterized. These materials combine to form the classical microstructure in battery electrodes for the electrical conductivity. It is demonstrated how new links to the BPCO, verified via materials-processing relationships, and the interim results are identified as intermediate data.

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain-specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data-driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology-based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.

This report is about the chemical formation of gels from ultrathin gold nanowires (AuNWs) and the gels’ properties. An excess of triphenylphosphine (PPh3) initiated the gelation of AuNWs with core diameters below 2 nm and an oleylamine (OAm) ligand shell dispersed in cyclohexane. The ligand exchange of OAm by PPh3 changes the AuNW-solvent interactions and leads to phase separation of the solvent to form a macroscopic gel. Small angle X-ray scattering and transmission electron microscopy indicate that hexagonal bundles in the original dispersion are dispersed, and the released nanowires entangle. Rheological analyses indicate that the resulting gel is stabilized both by physical entanglement and crosslinking of AuNWs by Van der Waals and π–π interactions. Chemically formed AuNW gels have solid-like properties and crosslinks that distinguish them from highly concentrated non-crosslinked AuNW dispersions. The AuNW gel properties can be tuned via the Au:PPh3 ratio, where smaller ratios led to stiffer gels with higher storage moduli.

Flexible hybrid electronics allow for seamless integration of sensing functionalities within materials, non-conformal surfaces and products and thus can enable novel value chains. Next to new functionalities, product authenticity plays a crucial role in complex global supply chains. This holds especially true, when products are deployed in critical environments, such as the industrial or automotive sector, where product failure can be fatal. In this work, we present a secure hybrid system, which contains a custom-designed, thinned ASIC in foil, as well as two printed temperature sensing elements that are seamlessly embedded in an industrial process fabricated automotive coolant hose and an inkjet-printed unique identifier in the form of a physically unclonable function to derive the system’s authenticity. We show the results of the standalone hose-integrated temperature sensors, the bulk ASIC verification results prior to thinning and foil integration, as well as the fully assembled integrated hybrid system. The thinned ASIC in foil communication interfaces, its circuit building blocks, as well as the integrated printed components were successfully commissioned. We show the obtained temperature response as well as the unique identification by generating the challenge response pairs of the physically unclonable function over 1000 repetitions. The security circuit shows only 0.0084% of flipped bits at T = 25 °C, which makes it well suited to be used as physically unclonable function.

The aggregation of carbon nanofillers within polymer matrices is a crucial phenomenon for the formation of conducting channels in electrically conductive composites. Herein, a systematic comparison of the effect of 1D and 2D carbon nanofillers, exploiting their dimension-dependent aggregation in matrices, is performed. The role of flexible matrix in fractal formation is also highlighted by demonstrating that the presence of polar moieties in a polymer matrix affects the agglomeration geometries of functional carbon nanomaterials. Carboxylic acid derivatives of nanotubes and hydroxylated graphene are incorporated into both “functionally rich” polyurethane and apolar polydimethylsiloxane matrices to explore filler–filler and matrix–filler interactions. The in situ ultra-small-angle X-ray scattering analysis performed with simultaneous conductivity measurements, and stretching of flexible film has established a distinct role for loading fraction of functional nanofillers in deciding the stability of conduction networks. The effect of topological differences in composites is observed to be most striking in the case of sheets, where it is shown that the 2D flakes can bend and unfold upon stress, exclusively affecting the percolation and conductive mechanism in composites. These findings help to select the suitable materials to design the next generation of flexible and wearable electronic devices, offering versatility and adaptability in applications.

Conductive polymer composites (CPCs) combine the stretchability of an elastomeric matrix with the electrical conductivity of a metallic filler. The 3D structure of this filler particle network (FPN) and the contact resistances between particles above percolation, key factors in the conductivity, are not well understood. Here, we introduce 3D reconstructions of FPNs of micron-sized spherical silver particles in polydimethylsiloxane from focused ion beam scanning electron microscopy tomography. Analysis of the tomographic images provides the length and number of parallel conductive paths. The results show that the average contact resistance drops five orders of magnitude when increasing the silver loading from 36 vol% to 53 vol%, highlighting its dominating role for macroscopic conductivity rather than network structure. This links to 33% larger average area-equivalent diameters of the contact spots. Diffusional tortuosity, a metric that quantifies flow restriction through narrow contact spots, proves that higher contact forces decrease current flow restrictions and thus, increase overall electrical conductivity. These conclusions are verified using a segregated CPC, and it is found that the addition of 20 vol%
of insulating fillers at a constant silver loading of 30 vol% increases the conductivity 37-fold and decreases the average contact resistance by two orders of magnitude.

Plant-inspired soft robots enable distributed environmental monitoring. Fliers, i.e. soft robots that are carried passively by the wind, can be effectively deployed and cover large areas and distances. State-of-the-art fliers for humidity sensing are largely composed of electronic components, which increase cost and generate electronic waste. Here, we introduce self-deployable and biodegradable fliers inspired by natural Ailanthus altissima seeds. These artificial fliers are composed of fluorescent, cellulose-based composites with sensing capabilities. The material is shaped into artificial seeds using scalable 3D extrusion processing. Red-emitting Mn2+-doped Er3+, Yb3+:NaYF4 nanoparticles in the composite provide a strong optical emission upon excitation at 980 nm wavelength. The cellulose matrix absorbs water, which quenches the intensity of fluorescence of the nanoparticles. Increasing humidity thus changes the color of the fluorescence emission from red to green. We used ratiometric sensing to detect the humidity of the surroundings.

Water-induced transparency loss in styrene–butadiene block copolymers (SBCs) has been investigated under a variety of conditions. Consistent with earlier work on homopolymers, the opacity after prolonged water exposure is expected to be caused by water clustering, which results from stronger water–water than water–polymer interactions. The water clusters distort the surrounding polymer matrix, causing local changes in the refractive index. It was found that the hard phase has only a minor contribution to the transparency loss, while the rubbery phase appears to be the major contributor. However, the loss of transparency was found not to be directly proportional to the volume of the soft phase, and a significant effect of the block copolymer morphology was observed, which was confirmed by a series of transmission electron microscopy and SAXS measurements. This effect is particularly evident in the transition from a continuous hard phase through a co-continuous morphology to a continuous soft phase. The acquired insights were subsequently used to predict long-term optical performance in SBCs to provide a tool in product development. Loss of transparency predictions was proven to be adequate through a classical regression-extrapolation approach using a limited data set, accurately simulating performance beyond 2600 h exposure time using only 600 h of measurement time. Additionally, it was shown that artificial neural networks could provide a solid tool in predicting performance even prior to synthesis, granted that the selection of descriptors is complete and the appropriate amount of data is supplied with a proper spread over the descriptor space.