High-performance soft–hard interfaces are inherently difficult to fabricate due to the dissimilar mechanical properties of both materials, especially when connecting extremely soft biomaterials, such as hydrogels, to much harder biomaterials, such as rigid polymers. Nevertheless, there is significant clinical demand for synthetic soft–hard interfaces. Here, soft–hard interface geometries are proposed, designed with the aid of computational analyses and fabricated as 3D-printed hydrogel-to-polylactide (PLA) structures. Two primary interlocking geometries (i.e., anti-trapezoidal (AT) and double-hook (DH)) are used to study the envelope of 2.5D geometric interlocking designs, fabricated through hybrid 3D printing, combining pneumatic extrusion with fused deposition modeling. Finite-element analysis, uniaxial tensile tests, and digital image correlation (DIC) are used to characterize the geometries and identify parameters that significantly influence their mechanical performance. These findings reveal significant differences between geometric designs, where DH geometries performed significantly better than AT geometries, exhibiting a 190% increase in the maximum force, F_max, and a 340% increase in the fracture toughness, W. Compared to the control groups (i.e., flat, inset, and 90° interfaces), F_max and W values increased by 500%–990% and 350%–1200%, respectively. The findings of this study can serve as a guideline for the design and fabrication of efficient soft–hard interfaces with performances close to predicted values.

Temporomandibular joint (TMJ) replacement prostheses often face limitations in accommodating translational movements, leading to unnatural kinematics and loading conditions, which affect functionality and longevity. Here, we investigate the potential of functionally graded materials (FGMs) in TMJ prostheses to enhance mandibular kinematics and reduce joint reaction forces. We develop a functionally graded artificial cartilage for the TMJ implant and evaluate five FGM designs: hard, hard-soft, and three FGM gradients with gradual transitions from 90% hard material to 0%, 10%, and 20%. These designs are 3D printed, mechanically tested under quasi-static compression, and simulated under physiological conditions. Results from computational modeling and experiments are compared to an intact mandible during incisal clenching and left group biting. The FGM design with a transition from 90% to 0% hard material improves kinematics by 19% and decreases perfomance by 3%, reduces joint reaction forces by 8% and 10%, and increases mandibular movement by 20% and 88% during incisal clenching and left group biting, respectively. These findings provide valuable insights for next-generation TMJ implants.

Orthopedic plates are commonly used after osteotomies for temporary fixation of bones. Patient-specific plates have recently emerged as a promising fixation device. However, it is unclear how various strategies used for the design of such plates perform in comparison with each other. Here, we compare the biomechanical performance of 3D printed patient-specific bone plates designed using conventional computer-aided design (CAD) techniques with those designed with the help of topology optimization (TO) algorithms, focusing on cases involving slipped capital femoral epiphysis (SCFE). We established a biomechanical testing protocol to experimentally assess the performance of the designed plates while measuring the full-field strain using digital image correlation. We also created an experimentally validated finite element model to analyze the performance of the plates under physiologically relevant loading conditions. The results indicated that the TO construct exhibited higher ultimate load and biomechanical performance as compared to the CAD construct, suggesting that TO is a viable approach for the design of such patient-specific bone plates. The TO plate also distributed stress more evenly over the screws, likely resulting in more durable constructs and improved anatomical conformity while reducing the risk of screw and plate failure during cyclic loading. Although differences existed between finite element analysis and experimental testing, this study demonstrated that finite element modelling can be used as a reliable method for evaluating and optimizing plates for SCFE patients. In addition to enhancing the mechanical performance of patient-specific fixation plates, the utilization of TO in plate design may also improve the surgical outcome and decrease the recovery time by reducing the plate and incision sizes.

Durable interfacing of hard and soft materials is a major design challenge caused by the ensuing stress concentrations. In nature, soft-hard interfaces exhibit remarkable mechanical performance, with failures rarely happening at the interface. Here, we mimic the strategies observed in nature to design efficient soft-hard interfaces. We base our geometrical designs on triply periodic minimal surfaces (i.e., Octo, Diamond, and Gyroid), collagen-like triple helices, and randomly distributed particles. A combination of computational simulations and experimental techniques, including uniaxial tensile and quad-lap shear tests, are used to characterize the mechanical performance of the interfaces. Our analyses suggest that smooth interdigitated connections, compliant gradient transitions, and either decreasing or constraining strain concentrations lead to simultaneously strong and tough interfaces. We generate additional interfaces where the abovementioned toughening mechanisms work synergistically to create soft-hard interfaces with strengths approaching the upper achievable limit and enhancing toughness values by 50%, as compared to the control group.

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled “Deep-DRAM” that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem.

Objective: Mandibular reconstruction using patient-specific cage implants is a promising alternative to the vascularized free flap reconstruction for nonirradiated patients with adequate soft tissues, or for patients whose clinical condition is not conducive to microsurgical reconstruction. This study aimed to assess the biomechanical performance of 3D printed patient-specific cage implants designed with a semi-automated workflow in a combined cadaveric and retrospective case series study. Methods: We designed cage implants for two human cadaveric mandibles using our previously developed design workflow. The biomechanical performance of the implants was assessed with the finite element analysis (FEA) and quasi-static biomechanical testing. Digital image correlation (DIC) was used to measure the full-field strains and validate the FE models by comparing the distribution of maximum principal strains within the bone. The retrospective study of a case series involved three patients, each of whom was treated with a cage implant of similar design. The biomechanical performance of these implants was evaluated using the experimentally validated FEA under the scenarios of both mandibular union and nonunion. Results: No implant or screw failure was observed prior to contralateral bone fracture during the quasi-static testing of both cadaveric mandibles. The FEA and DIC strain contour plots indicated a strong linear correlation (r = 0.92) and a low standard error (SE=29.32με), with computational models yielding higher strain values by a factor of 2.7. The overall stresses acting on the case series’ implants stayed well below the yield strength of additively manufactured (AM) commercially pure titanium, when simulated under highly strenuous chewing conditions. Simulating a full union between the graft and remnant mandible yielded a substantial reduction (72.7±1.5%) in local peak stresses within the implants as compared to a non-bonded graft. Conclusions: This study shows the suitability of the developed semi-automated workflow in designing patient-specific cage implants with satisfactory mechanical functioning under demanding chewing conditions. The proposed workflow can aid clinical engineers in creating reconstruction systems and streamlining pre-surgical planning. Nevertheless, more research is still needed to evaluate the osteogenic potential of bone graft insertions.

Living organisms use functional gradients (FGs) to interface hard and soft materials (e.g., bone and tendon), a strategy with engineering potential. Past attempts involving hard (or soft) phase ratio variation have led to mechanical property inaccuracies because of microscale-material macroscale-property nonlinearity. This study examines 3D-printed voxels from either hard or soft phase to decode this relationship. Combining micro/macroscale experiments and finite element simulations, a power law model emerges, linking voxel arrangement to composite properties. This model guides the creation of voxel-level FG structures, resulting in two biomimetic constructs mimicking specific bone-soft tissue interfaces with superior mechanical properties. Additionally, the model studies the FG influence on murine preosteoblast and human bone marrow-derived mesenchymal stromal cell (hBMSC) morphology and protein expression, driving rational design of soft-hard interfaces in biomedical applications.

The reconstruction of large mandibular defects with optimal aesthetic and functional outcomes remains a major challenge for maxillofacial surgeons. The aim of this study was to design patient-specific mandibular reconstruction implants through a semi-automated digital workflow and to assess the effects of topology optimization on the biomechanical performance of the designed implants. By using the proposed workflow, a fully porous implant (LA-implant) and a topology-optimized implant (TO-implant) both made of Ti-6Al-4V ELI were designed and additively manufactured using selective laser melting. The mechanical performance of the implants was predicted by performing finite element analysis (FEA) and was experimentally assessed by conducting quasi-static and cyclic biomechanical tests. Digital image correlation (DIC) was used to validate the FE model by comparing the principal strains predicted by the FEM model with the measured distribution of the same type of strain. The numerical predictions were in good agreement with the DIC measurements and the predicted locations of specimen failure matched the actual ones. No statistically significant differences (p < 0.05) in the mean stiffness, mean ultimate load, or mean ultimate displacement were detected between the LA- and TO-implant groups. No implant failures were observed during quasi-static or cyclic testing under masticatory loads that were substantially higher (>1000 N) than the average maximum biting force of healthy individuals. Given its relatively lower weight (16.5%), higher porosity (17.4%), and much shorter design time (633.3%), the LA-implant is preferred for clinical application. This study clearly demonstrates the capability of the proposed workflow to develop patient-specific implants with high precision and superior mechanical performance, which will greatly facilitate cost- and time-effective pre-surgical planning and is expected to improve the surgical outcome.

4D printing of flat sheets that self-fold into architected 3D structures is a powerful origami-inspired approach for the fabrication of multi-functional devices and metamaterials. However, the opposite stiffness requirements for the folding process and the subsequent loadbearing of 3D structures impose an intrinsic limitation in designing self-folding metamaterials: while a low stiffness is required for the successful completion of the self-folding step, a high stiffness is needed for utilizing the folded structure as a load-bearing mechanical metamaterial. Here, we present a nonlinear analytical model of self-folding bilayer constructs composed of an active and passive layer. This finite-deformation theoretical model predicts the curvature of activated bilayers, establishes their stability limits, and estimates the stiffness of folded bilayers, yielding the theoretical stiffness limits of self-folding bilayers. We use our model to identify the optimal combinations of geometrical and mechanical properties that result in the highest possible stiffness of folded constructs. We then compare the predictions of our analytical model with computational results, and validate our theory with experimental realizations of 4D printed structures. Finally, we evaluate the theoretical stiffness limits of bilayer constructs made using the most common types of stimuli-responsive materials. Our analysis shows that a maximum effective modulus of ≈ 1.5 GPa can be achieved using the currently available shape-memory polymers.

Background: Nuclear factor erythroid 2–related factor 2 (Nrf2) is a crucial transcription factor for cellular redox homeostasis. The association of Nrf2 with elderly female osteoporotic has yet to be fully described. The aim was to elucidate a potential age-dependent Nrf2 contribution to female osteoporosis in mice. Methods: Eighteen female wild type (WT) and 16 Nrf2-knockout (KO) mice were sacrificed at different ages (12 weeks = young mature adult and 90 weeks = old) to analyze their femurs. The morphological properties (trabecular and cortical) were evaluated by micro-computed tomography (μCT) and compared to gold standard histochemistry analysis. The quasi-static compression tests were performed to calculate the mechanical properties of bones. Additionally, the population of bone resorbing cells and aromatase expression by osteocytes was immunohistochemically evaluated and empty osteocyte lacunae was counted in cortical bone. Results: Old Nrf2-KO mice revealed a significantly reduced trabecular bone mineral density (BMD), cortical thickness, cortical area, and bone fraction compared to old WT mice, regardless of no significant difference in skeletally mature young adult mice between WT and KO. Specifically, while all old WT mice showed thin metaphyseal trabeculae, trabecular bone was completely absent in 60% of old KO mice. Additionally, old KO mice showed significantly more osteoclast-like cells and fewer aromatase-positive osteocytes than WT mice, whereas the occurrence of empty osteocyte lacunae did not differ between both groups. Nrf2-KO mice further showed an age-dependently reduced fracture resilience compared to age-matched WT mice. Conclusion: Our results suggest that chronic Nrf2 loss can lead to age-dependent progression of female osteoporosis.

Bio-inspired composites are a great promise for mimicking the extraordinary and highly efficient properties of natural materials. Recent developments in voxel-by-voxel 3D printing have enabled extreme levels of control over the material deposition, yielding complex micro-architected materials. However, design complexity, very large degrees of freedom, and limited computational resources make it a formidable challenge to find the optimal distribution of both hard and soft phases. To address this, a nonlinear coarse-graining approach is developed, where foam-based constitutive equations are used to predict the elastoplastic mechanical behavior of biomimetic composites. The proposed approach is validated by comparing coarse-grained finite element predictions against full-field strain distributions measured using digital image correlation. To evaluate the degree of coarse-graining on model accuracy, pre-notched specimens decorated with a binarized version of a renowned painting were modeled. Subsequently, coarse-graining is used to predict the fracture behavior of bio-inspired composites incorporating complex designs, such as functional gradients and hierarchical organizations. Finally, as a showcase of the proposed approach, the inverse coarse-graining is combined with a theoretical model of bone tissue adaptation to optimize the microarchitecture of a 3D-printed femur. The predicted properties were in exceptionally good agreement with the corresponding experimental results. Therefore, the coarse-graining method allows the design of advanced architected materials with tunable and predictable properties.

Emerging multi-material 3D printing techniques enables the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials within those geometries, yielding unique combinations of elastic properties. However, discovering the rare designs that lead to highly unusual combinations of material properties, such as double-auxeticity and high elastic moduli, remains a non-trivial crucial task. Here, we use computational models and deep learning algorithms to identify rare-event designs. In particular, we study the relationship between random distributions of hard and soft phases in three types of planar lattices and the resulting mechanical properties of the two-dimensional networks. By creating a mapping from the space of design parameters to the space of mechanical properties, we are able to reduce the computational time required for evaluating each design to ≈2.4 × 10⁻⁶ s, and to make the process of evaluating different designs highly parallelizable. We then select ten designs to be 3D printed, mechanically test them, and characterize their behavior using digital image correlation to validate the accuracy of our computational models. Our simulation results show that our deep learning-based algorithms can accurately predict the mechanical behavior of the different designs and that our modeling results match experimental observations.