Biomimetics

From Palaeolithic ornaments to modern biomimetics, the use of nacre and shells has evolved. Initially utilised for jewellery and tools, they now inspire the development of advanced materials. This paper reviews the current knowledge on nacre’s composition, focusing on the highly regulated biomineralisation process wherein amorphous calcium carbonate (ACC) transforms into crystalline aragonite. It examines the important role of the organic matrix (specifically soluble, insoluble, and acidic proteins) in controlling crystal nucleation, growth, and polymorph selection. Scientists study natural nacre formation to create nacre-inspired composites for various applications. Charles Hatchett’s in 1799 shell categorisation, Sorby and Sowerby’s 19th-century microscopy, Taylor, Beedham, Bøggild, and Currey’s mid-20th-century research on bivalve structures, and mechanical property investigations in the 1970s are some of the major developments. The hierarchical structure, cooperative plastic deformation, surface asperities, organic–inorganic interactions, and interphase in such complex composite materials give rise to impressive mechanical properties. In the early 2000s, with the emergence of biomimetics, inspired by nacre, several macroscopic structural materials with uniform micro- and nanoscale architectures have been synthesised in recent decades, and their mechanical properties and potential applications have been explored. Modern nacre-inspired fabrication utilises 3D printing for precision, freeze casting for sustainability, and mineralisation for scalability. Techniques like layer-by-layer assembly and nanomaterial integration enhance mechanical performance through advanced interfacial engineering.

The NerveRepack project is a European initiative that aims to develop biomimetic exoskeletons/exoprostheses for amputated or paralyzed leg patients that will receive and transmit signals to enable movements and sensations for the patient. To implement the project, it is fundamental to develop implantable neuronal electrodes that will allow bidirectional signaling between the sensors placed on the exoskeletons/exoprostheses and the nervous system. In this direction, two electrodes, plug and cuff, have been designed as integral parts of the final implantable device. The electrodes should comply with strict regulations to ensure their safe implantation in patients. The purpose of this study was to support the compliance of the implant platforms of certain key components with the ISO and ASTM standards that would be required for clinical applications. We have used an indirect method to assess the biocompatibility of the developed electrodes against neuronal cells, fibroblasts, and keratinocytes. Also, we assessed hemocompatibility, i.e., the potential of implantable electrodes to induce hemolysis or complement activation. Finally, the mutagenic/genotoxic potential was tested against the internationally recommended CHO cells. Both representative plug and cuff electrode components were found non-cytotoxic, non-mutagenic, and unable to induce hemolysis. Therefore, from the point of early evaluation of in vitro material and process biocompatibility, the selected implant platforms for the electrodes could be implanted in preclinical models to delineate their potential in vivo applications as neuronal interface with the biomimetic exoskeleton/exoprostheses.

Artificial Intelligence (AI) has introduced transformative possibilities in orthodontics by enhancing diagnostic precision, treatment planning, and aesthetic outcomes. In face-driven orthodontics, treatment objectives extend beyond achieving proper occlusion to optimizing facial balance and harmony. With the growing patient demand for aesthetic improvements, AI technologies enable clinicians to integrate facial analysis and dynamic soft-tissue evaluation into personalized treatment approaches. Research in this scoping review analyzed current applications of AI in face-driven orthodontics, focusing on diagnosis, soft-tissue assessment, and individualized treatment planning. A comprehensive search was conducted in PubMed and Scopus for studies published between 2021 and 2025. The review followed the PRISMA-ScR guidelines. Of 54 initially identified studies, 24 met the inclusion criteria after title, abstract, and full-text screening. Extracted data were organized according to the main application areas of AI in face-driven orthodontics. Most studies focused on AI-assisted facial analysis, 3D reconstruction, and treatment simulation. Deep learning models demonstrated high performance in soft-tissue prediction, aesthetic evaluation, and diagnostic accuracy. However, heterogeneity in datasets, a lack of standardized validation protocols, limited external validation across included studies and limited clinical applicability were identified as key limitations. AI-based facial analysis supports a shift toward individualized, aesthetics-oriented orthodontic planning. Although current evidence highlights its potential for improving diagnostic precision and treatment outcomes, further validation through large-scale clinical studies is essential for broader implementation in everyday practice.