Universidad Carlos III de Madrid (UC3M), in collaboration with the University of Oxford, Imperial College London and the BC Materials research centre in the Basque Country, has developed an innovative computational model that makes it possible to predict and improve the behaviour of multifunctional structures manufactured using 3D printers. This breakthrough, supported by the BBVA Foundation through a Leonardo Grant and recently published in the journal Nature Communications, opens the door to new applications in sectors such as biomedicine, soft robotics and other branches of engineering.

“Currently, conductive thermoplastics are very promising because of their ability to transmit electrical signals while providing structural support,” explains one of the study's authors, Daniel García-González, from the UC3M Department of Mechanics of Continuous Media and Theory of Structures. “But the main challenge in the manufacture of these materials is the control of their internal structure, since the bonding between filaments and the presence of small cavities affect both their mechanical resistance and their capacity to transmit electrical signals,” explains the scientist.

Until now, these factors were considered unavoidable shortcomings of the 3D printing process. However, the researchers have managed to control these characteristics by integrating advanced computational tools and experimental trials, which has allowed them to manufacture structures that are sensitive and capable of transforming mechanical signals into electrical signals.

“A key point about this discovery is that it can be extrapolated to other types of 3D printing technology in which softer materials could be used,” adds Javier Crespo, also from UC3M's Department of Mechanics of Continuous Media and Theory of Structures. The researcher is optimistic that it will be possible to design materials that lay the foundations for future advances in additive manufacturing, thanks to the combination of these new computational tools.

This new research, backed up by extensive experimental validation, provides a reliable approach to minimising the differences between the different behaviours of conductive components and represents a major step forward in the design of multifunctional materials, according to its authors. “For example, in the field of engineering, these structures could be used both for the manufacture of soft robots and for obtaining virtual data that can serve machine learning technologies,” notes Javier Crespo.

Emilio Martínez-Pañeda, professor at the University of Oxford and co-author of the study, pointed out that “the research opens up endless opportunities, enabling the development of intelligent materials and sensors that could be of great use in the aerospace industry or in infrastructure monitoring.”

“And not only that,” adds Daniel García-González, “with these new materials we could also create patches or dressings that warn us how many times we are flexing our knee so that, in the event that we have an injury, we are alerted if we are passing certain critical points where we are going to cause damage to our muscles.”