Augmented semantic segmentation for the digitization of grinding tools based on deep learning

In order to analyze various process characteristics, grinding simulations can be used, which need accurate models of the tool and the individual grains. For this purpose, grinding tools can be digitized. To identify characteristic grains from a large number of measurements, each individual grain has to be analyzed and separated from the bond manually. Therefore, a deep learning-based methodology was developed to achieve a high segmentation accuracy of the grain boundaries efficiently. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. The model transferability was quantified by analyzing different states of tool wear.     

Water Sorption and Diffusion in (Reduced) Graphene Oxide‐Alginate Biopolymer Nanocomposites

The water sorption and diffusion in (reduced) graphene oxide‐alginate composites of various compositions is analyzed. Water sorption of sodium alginate can be significantly reduced by the inclusion of graphene oxide sheets due to the formation of an extensive hydrogen bonding network between oxygenated groups. Crosslinking alginate with divalent metal ions and the presence of reduced graphene oxide can further improve the swelling resistance due to the strong interactions between metal ions, alginate, and filler sheets. Depending on conditions and composition, the overall water barrier properties of alginate composites improve upon (reduced) graphene oxide filling, making them attractive for moisture barrier coating applications. Water sorption kinetics in all alginate composites indicate a non‐Fickian diffusion process that can be accurately described by the Variable Surface Concentration model. In addition, the water barrier properties of sodium alginate‐graphene oxide composites can be adequately predicted using a simple model that takes the orientational order of filler sheets and their effective aspect ratio into account.

     

Silk proteins for biomedical applications: Bioengineering perspectives

Biomaterials of either natural or synthetic origin are used to fabricate implantable devices, as carriers for bioactive molecules or as substrates to facilitate tissue regeneration. For the design of medical devices it is fundamental to use materials characterized by non-immunogenicity, biocompatibility, slow and/or controllable biodegradability, non-toxicity, and structural integrity. The success of biomaterial-derived biodevices tends to be based on the biomimetic architecture of the materials. Recently, proteins from natural precursors that are essentially structural and functional polymers, have gained popularity as biomaterials. The silks produced by silkworms or spiders are of particular interest as versatile protein polymers. These form the basis for diverse biomedical applications that exploit their unique biochemical nature, biocompatibility and high mechanical strength. This review discusses and summarizes the latest advances in the engineering of silk-based biomaterials, focusing specifically on the fabrication of diverse bio-mimetic structures such as films, hydrogels, scaffolds, nanofibers and nanoparticles; their functionalization and potential for biomedical applications.