Prof. Dr. Tobias Kraus

Accurate reconstruction of the conductive networks in lithium-ion battery electrodes is essential for understanding material-structure-property relationships and optimizing manufacturing processes. The resolution of these networks remains a challenge due to the average size of the microstructure features and lack of contrast to the other materials generally present. Here, a digital materials framework for the three-dimensional reconstruction and quantitative analysis of lithium-ion battery cathode microstructure using focused ion beam–scanning electron microscopy (FIB–SEM) tomography with segmentation by machine learning is introduced. Low acceleration voltage image stacks are recorded and compared to identify the optimal voltage. Manual segmentation aided with standard software provides a ground truth that is used to train a deep neural network model to identify the phases of a cathode material. The model is applied for robust identification and reconstruction of conductive phases. Quantified microstructural descriptors are extracted from the automatic segmentation and systematically linked to structured processing parameters within a digital platform, DataCharge.io, enabling consistent comparison across lab- and pilot-scale electrodes. This integrated data-driven approach facilitates targeted optimization of electrode fabrication, supports transferable process–structure correlations, and advances digitalization strategies for battery materials engineering.

Special Issue

Conductive polymer composites for printed stretchable temperature sensors must remain functional during many strain cycles despite unavoidable changes in their microstructure and thus the electrically conductive network. In this study, we introduce screen-printable inks with electrical conductivities and temperature coefficients of resistance (TCR, α) that are suitable for application in temperature sensors while retaining thermal and mechanical stability upon stretching the printed sensors. Silver flakes (AgF) and carbon black (CB) or graphite flakes (GR) at different ratios were combined as conductive fillers in dragon skin (DS) elastomer and screen printed. Their electrothermal properties were characterized within the temperature range of 20°C–120°C. Samples with constant α in suitable ranges were selected and characterized electrically for stability during thermal cycling and mechanically during 30 000 strain cycling. The DS-AgF-CB12-180°C and DS-AgF-GR10-180°C composites exhibited linear resistance changes with α = 2.29 × 10−3/°C and 1.75 × 10−3/°C, respectively. The resistance of DS-AgF-GR drifted below 2% over 10 thermal cycling and relative resistance changes (R/R0) increased up to 8 after 30 000 strain cycling at 0%–10%. We show that the carbon particles modify the metal–matrix interactions in the hybrid composites, thus increasing stability and reliability of temperature sensing.

Combinations of two different colloidal nanostructures can inherit properties of both and give access to new, emerging properties. Here, we create optically downconverting heteroaggregates of carbon quantum dots and lanthanide doped nanocrystals with high quantum yields and stability against blinking and bleaching. These heteroaggregates are heavy metal-free and display visible emissions that are suitable for multimodal sensing. Red emitting europium Eu3+ doped CaF2 nanoparticles were combined with orange emitting carbon quantum dots prepared by solvothermal reactions. Simple mixing led to the formation of heteroaggregates as confirmed by dynamic light scattering and zeta potential analysis. The assembly caused electromagnetic coupling, changed the particles' response to temperature and pH in aqueous dispersion, and increased the sensing performance. Emission dropped by 39% at 550 nm (of carbon quantum dots) and 8% at 613 nm (of Eu3+ doped CaF2 nanocrystals) from 17% and 33% of the pure nanoparticle dispersions. Changing pH from 1.3 to 12.5 quenched emissions by 92% and 74% at the same wavelengths, increasing from 62% and 47% of the pure dispersions. Ratiometric calibrations based on the intensity ratio of the 550 nm and 613 nm emissions were constructed to use the heteroaggregates as sensors. They performed as optical thermometers in the range from 293 K to 333 K with a temperature resolution of 0.35 K, and pH values within a range from 1.3 to 12.5 with a resolution of 0.15 pH units. Our results show that heteroaggregates of coupled nanostructures provide improved performance in temperature and pH sensing.

Salts induce the agglomeration and assembly of gold nanocrystals that are stabilized by charged ligand shells. This destabilization is known to partially deviate from the predictions of classical DLVO theory for larger colloids, but existing studies focus on limited concentration ranges or ion types. Here, we use a high-throughput approach to test the agglomeration efficacy of 17 different salts at concentrations ranging from 0.16 mM to 2 M on negatively charged nanocrystals with shells of 11-mercaptoundecanoic acid and/or triethylene glycol mono-11-mercaptoundecyl ether. Automated pipetting is used to create a large dataset of close to 10000 UV–vis absorbance spectra. We analyze the spectral shifts to find the onset of agglomeration, identify critical salt concentrations, and characterize the nature of the agglomeration transition. The results are compared to classical DLVO theory using conventional analysis, and the effects of ion concentration and anion valency are shown to be consistent with DLVO predictions. Cation valencies only partially follow the predictions, suggesting local ion-specific interactions that dominate when screening lengths reach molecular length scales. A Random Forest Regression model is used as additional “black box” analysis of the results to correlate the ionic strength, ligand shell composition, and intrinsic ion properties, and to rank their relative importance to the colloidal stability of gold nanocrystals. The ranking combines classical DLVO effects and specific ion interactions with subtle effects on the agglomerate structure that affect plasmon resonance shifts, providing a complementary interpretation of the data.

Metallic filler particles form continuous paths in nonconductive elastomers and provide electrical conduction. The resulting conductive polymer composites (CPCs) are useful as flexible and stretchable conductors or strain sensors. Percolation theory accurately describes the changes in conductivity close to a critical loading φC but fails to predict conductivities above this transition. Here, we show that graph theory (GT) metrics can be used to correlate network structure and macroscopic electrical conductivity above φC. We used FIB-SEM tomography to reconstruct (31.5 μm)3 large CPC volumes with 2.5-μm-diameter silver spheres at loadings between 27 and 52 vol%. We find linear correlations between the number of nodes and edges and the average graph degree, length, efficiency, and current-flow betweenness with the conductivity of CPCs. We formulate a simple model that describes the increase in conductivity above φC in terms of network morphology. Kirchhoff circuit analysis reveals that the change in network topology alone cannot explain the experimentally observed conductivity. We show that the average particle–particle contact resistance scales reciprocally with degree. This suggests that the filler loading affects contact areas or tunneling widths, providing a link between mechanical and electrical network properties.

A low-temperature sintering mechanism of silver microparticles is established and used to enable the design-for-recycling of printed electronics. The formation of necks during the initial phase sintering of precipitated and atomized silver microparticles is studied. Temperature- and time-dependent in-situ analyses indicate the existence of a mobile silver species that provides efficient mass transport. The activation energy of neck formation identifies silver ion formation as the rate-limiting step of low-temperature silver sintering. It is demonstrated that resistivities of 271 times that of bulk silver can be attained after 40 minutes at 150°C. Low-temperature sintering not only reduces the energy required during thermal treatment but it yields layers that are suitable for recycling, too. The resulting layers have conductive necks that are mechanically weak enough to be broken during recycling. Printed layers are redispersed and the recycled silver powder is reused without loss of the electrical performance in new prints. Their conductivities are industrially relevant, which makes this recyclability-by-design approach promising for manufacturing more sustainable printed electronics.

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.