We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 min of hands-on time by a user and can typically be finished within less than 12 h when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO₂ particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.

The recent emergence of self-driving laboratories (SDL) and material acceleration platforms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting contributions. Here, we describe in detail MINERVA, an SDL specifically built and designed for the synthesis, purification, and in line characterization of nano- and advanced materials. By fully automating these three process steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core-shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. We further study the reproducibility and particle size distributions of these seven representative materials in depth and show the excellent performance of the platform when synthesizing these material classes. Lastly, we discuss the design considerations as well as the hardware and software components that went into building the platform and make all of the components publicly available.

The rising threat from infectious pathogens poses an ever-growing challenge. Metal-based nanomaterials have gained a great deal of attention as active components in antimicrobial coatings. Here, we report on the development of readily deployable, sprayable antimicrobial surface coatings for high-touch stainless steel surfaces that are ubiquitous in many healthcare facilities to combat the spread of pathogens. We synthesized mesoporous silica nanoparticles (MSNs) with different surface functional groups, namely, amine (MSN-NH₂), carboxy (MSN-COOH), and thiol groups (MSN-SH). These were chosen specifically due to their high affinity to copper and silver ions, which were used as antimicrobial payloads and could be incorporated into the mesoporous structure through favorable host-guest interactions, allowing us to find the most favorable combinations to achieve antimicrobial efficacy against various microbes on dry or semidry high-touch surfaces. The antimicrobial MSNs were firmly immobilized on stainless steel through a simple two-step spray-coating process. First, the stainless steel surfaces are primed with sprayable polyelectrolyte solutions acting as adhesion layers, and then, the loaded nanoparticle dispersions are spray-coated on top. The employed polyelectrolytes were selected and functionalized specifically to adhere well to stainless steel substrates while at the same time being complementary to the MSN surface groups to enhance the adhesion, wettability, homogeneity, and stability of the coatings. The antimicrobial properties of the nanoparticle suspension and the coatings were tested against three commonly found pathogenic bacteria, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, as well as a fungal pathogen, Candida albicans. Especially MSN-SH loaded with silver ions showed excellent antimicrobial efficacy against all tested pathogens under application-relevant, (semi)dry conditions. The findings obtained here facilitate our understanding of the correlation between the surface properties, payloads, and antimicrobial activity and show a new pathway toward simple and easily deployable solutions to combat the spread of pathogens with the help of sprayable antimicrobial surface coatings.

Custom large language models are used to extract structured data from synthesis procedures given in natural language to automatically generate knowledge graphs and build workflows for executing syntheses on Self-Driving Labs.