Over millennia, bacteria have developed clever strategies to build biopolymer‐based communities in which they can survive even extremely challenging conditions. Such bacterial biofilms come with a broad range of fascinating material properties that—in settings such as medicine, food production, or other areas of industry—make it difficult to remove or inactivate them: they can stick to many surfaces, repel water and oils, and can even transport electrons. Inspired by the outstanding versatility and sturdiness of such bacterial biofilms, material scientists have set out to harness those properties and to create bacterial materials for different applications. However, as the range of technological applications employing biofilms keeps expanding, improved material properties or broader functionalities are desired. Here, such attempts where materials with improved properties were created by making use of either natural or modified bacterial biofilms are reviewed. The areas in which those bacterial materials may be used range from agriculture and (environmental) biotechnology over biomedical and electrical engineering to construction engineering.

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fastevolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by timeconsuming trial and error approaches and our limited understanding of how different material properties depend on each other. Here, we apply machine learning algorithms to classify complex biological materials based on their microtopography. With this approach, the surfaces of different variants of biofilms and plant leaves can not only be distinguished but also correctly classified according to their wettability. Furthermore, an importance ranking provided by one of the algorithms allows us to identify those surface features that are critical for a successful sample classification. Our study exemplifies how machine learning can contribute to the analysis and categorization of complex surfaces, a tool, which can be highly useful for other areas of materials science, such as damage assessment as well as adhesion or friction studies.

Most biofilm research has so far focused on investigating biofilms generated by single bacterial strains. However, such single-species biofilms are rare in nature where bacteria typically coexist with other microorganisms. Although, from a biological view, the possible interactions occurring between different bacteria are well studied, little is known about what determines the material properties of a multi-species biofilm. Here, we ask how the co-cultivation of two B. subtilis strains affects certain important biofilm properties such as surface topography and wetting behavior. We find that, even though each daughter colony typically resembles one of the parent colonies in terms of morphology and wetting, it nevertheless exhibits a significantly different surface topography. Yet, this difference is only detectable via a quantitative metrological analysis of the biofilm surface. Furthermore, we show that this difference is due to the presence of bacteria belonging to the ‘other’ parent strain, which does not dominate the biofilm features. The findings presented here may pinpoint new strategies for how biofilms with hybrid properties could be generated from two different bacterial strains. In such engineered biofilms, it might be possible to combine desired properties from two strains by co-cultivation.

By incorporating the macromolecule PGA into the matrix of B. subtilis biofilms, the superhydrophobobic properties of the material are enhanced.