Digital light 3D printing of a polymer composite featuring robustness, self-healing, recyclability and tailorable mechanical properties.

Producing lightweight structures with high weight-specific strength and stiffness, self-healing abilities, and recyclability, is highly attractive for engineering applications such as aerospace, biomedical devices, and smart robots. Most self-healing polymer systems used to date for mechanical components lack 3D printability and satisfactory load-bearing capacity. Here, we report a new self-healable polymer composite for Digital Light Processing 3D Printing, by combining two monomers with distinct mechanical characteristics. It shows a desirable and superior combination of properties among 3D printable self-healing polymers, with tensile strength and elastic modulus up to 49 MPa and 810 MPa, respectively. Benefiting from dual dynamic bonds between the linear chains, a healing efficiency of above 80% is achieved after heating at a mild temperature of 60 °C without additional solvents. Printed objects are also endowed with multi-materials assembly and recycling capabilities, allowing robotic components to be easily reassembled or recycled after failure. Mechanical properties and deformation behaviour of printed composites and lattices can be tuned significantly to suit various practical applications by altering formulation. Lattice structures with three different architectures were printed and tested in compression: honeycomb, re-entrant, and chiral. They can regain their structural integrity and stiffness after damage, which is of great value for robotic applications. This study extends the performance space of composites, providing a pathway to design printable architected materials with simultaneous mechanical robustness/healability, efficient recoverability, and recyclability.

The Virtual Nose: Assessment of Static Nasal Airway Obstruction Using Computational Simulations and 3D-Printed Models.

Background: The use of virtual noses to predict the outcome of surgery is of increasing interests, particularly, as detailed and objective pre- and postoperative assessments of nasal airway obstruction (NAO) are difficult to perform. The objective of this article is to validate predictions using virtual noses against their experimentally measured counterpart in rigid 3D-printed models. Methods: Virtual nose models, with and without NAO, were reconstructed from patients' cone beam computed tomography scans, and used to evaluate airflow characteristics through computational fluid dynamics simulations. Prototypes of the reconstructed models were 3D printed and instrumented experimentally for pressure measurements. Results: Correlation between the numerical predictions and experimental measurements was shown. Analysis of the flow field indicated that the NAO in the nasal valve increases significantly the wall pressure, shear stress, and incremental nasal resistance behind the obstruction. Conclusions: Airflow predictions in static virtual noses correlate well with detailed experimental measurements on 3D-printed replicas of patient airways.