Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.

The 3D nano/microstructure of materials can significantly influence their macroscopic properties. In order to enable a better understanding of such structure-property relationships, 3D microscopy techniques can be deployed, which are however often expensive in both time and costs. Often 2D imaging techniques are more accessible, yet they have the disadvantage that the 3D nano/microstructure of materials cannot be directly retrieved from such measurements. The motivation of this work is to overcome the issues of characterizing 3D structures from 2D measurements for hetero-aggregate materials. For this purpose, a method is presented that relies on machine learning combined with methods of spatial stochastic modeling for characterizing the 3D nano/microstructure of materials from 2D data. More precisely, a stochastic model is utilized for the generation of synthetic training data. This kind of training data has the advantage that time-consuming experiments for the synthesis of differently structured materials followed by their 3D imaging can be avoided. More precisely, a parametric stochastic 3D model is presented, from which a wide spectrum of virtual hetero-aggregates can be generated. Additionally, the virtual structures are passed to a physics-based simulation tool in order to generate virtual scanning transmission electron microscopy (STEM) images. The preset parameters of the 3D model together with the simulated STEM images serve as a database for the training of convolutional neural networks, which can be used to determine the parameters of the underlying 3D model and, consequently, to predict 3D structures of hetero-aggregates from 2D STEM images. Furthermore, an error analysis is performed with respect to structural descriptors, e.g. the hetero-coordination number. The proposed method is applied to image data of TiO₂-WO₃ hetero-aggregates, which are highly relevant in photocatalysis processes. However, the proposed method can be transferred to other types of aggregates and to different 2D microscopy techniques. Consequently, the method is relevant for industrial or laboratory setups in which product quality is to be quantified by means of inexpensive 2D image acquisition.

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%.

Spray-drying is a flexible method for creating fine porous composites with controlled size and morphology. This study investigates how the morphology and porosity of the spray-dried powder of nano-alumina and polyvinylpyrrolidone (PVP-30) granules are affected by both the feed rate and the binder concentration. Droplet size and velocity distributions, measured with a HiWatch system, showed that higher feed rates produce larger droplets with faster velocities, therefore affecting the final morphology of the dried product. The morphology of the dried granules was analyzed using inline SOPAT imaging. While mercury intrusion porosimetry quantified the nano-pore volume and nano-pore size of the granules, offline scanning electron microscopy (SEM) was also used to characterize the morphology of the dried product. The findings show that, while raising the binder concentration produces a more compact morphology with a lower nano-pore volume, higher feed rates produce larger granules with a larger nano-pore volume. This study offers fundamental insights that can support the future development of control strategies for optimizing the production of spray-dried porous alumina–polymer nanocomposites by means of knowledge about the relationship between these process parameters and product qualities.

Catalysis, particularly heterogeneous catalysis, is crucial in the chemical industry and energy storage. Approximately 80% of all chemical products produced by heterogeneous catalysis are produced by solid catalysts, which are essential for the synthesis of ammonia, methanol, and hydrocarbons. Despite extensive use, challenges in catalyst development remain, including enhancing selectivity, stability, and activity. These effective properties are influenced by the nanoscale morphology of the catalysts, whereby the size of the nanoparticles is only one key descriptor. To investigate the relationship between nanoparticle morphology and catalytic performance, a comprehensive 3D analysis of nano-scale catalyst particles is necessary. However, traditional imaging techniques for a representative recording of this size range, such as transmission electron microscopy (TEM), are mostly limited to 2D. Thus, in the present paper, a stochastic 3D model is developed for a data-driven analysis of the nanostructure of catalyst particles. The calibration of this model is achieved using 2D TEM data from two different length scales, allowing for a statistically representative 3D modeling of catalyst particles. Furthermore, digital twins of catalyst particles can be drawn for the stochastic 3D model for virtual materials testing, enhancing the understanding of the relationship between catalyst nanostructure and performance.