Effect of serrated grain boundary on tensile and creep properties of a precipitation strengthened high entropy alloy.

In this study, tensile and creep deformation of a high-entropy alloy processed by selective laser melting (SLM) has been investigated; hot ductility drop was identified at first, and the loss of ductility at elevated temperature was associated with intergranular fracture. By modifying the grain boundary morphology from straight to serration, the hot ductility drop issue has been resolved successfully. The serrated grain boundary could be achieved by reducing the cooling rate of solution heat treatment, which allowed the coarsening of L12 structured γ' precipitates to interfere with mobile grain boundaries, resulting in undulation of the grain boundary morphology. Tensile and creep tests at 650°C were conducted, and serrated grain boundary could render a significant increase in tensile fracture strain and creep rupture life by a factor of 3.5 and 400, respectively. Detailed microstructure analysis has indicated that serrated grain boundary could distribute strains more evenly than that of straight morphology. The underlying mechanism of deformation with grain boundary serration was further demonstrated by molecular dynamic simulation, which has indicated that serrated grain boundaries could reduce local strain concentration and provide resistance against intergranular cracking. This is the first study to tackle the hot ductility drop issue in a high-entropy alloy fabricated by SLM; it can provide a guideline to develop future high-entropy alloys and design post heat treatment for elevated temperature applications.

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network.

Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.

Healing Pattern Analysis for Dental Implants Using the Mechano-Regulatory Tissue Differentiation Model.

(1) Background: Our aim is to reveal the influence of the geometry designs on biophysical stimuli and healing patterns. The design guidelines for dental implants can then be provided. (2) Methods: A two-dimensional axisymmetric finite element model was developed based on mechano-regulatory algorithm. The history of tissue differentiation around eight selected implants can be predicted. The performance of the implants was evaluated by bone area (BA), bone-implant contact (BIC); (3) Results: The predicted healing patterns have very good agreement with the experimental observation. Many features observed in literature, such as soft tissues covering on the bone-implant interface; crestal bone loss; the location of bone resorption bumps, were reproduced by the model and explained by analyzing the solid and fluid biophysical stimuli and (4) Conclusions: The results suggested the suitable depth, the steeper slope of the upper flanks, and flat roots of healing chambers can improve the bone ingrowth and osseointegration. The mechanism related to solid and fluid biophysical stimuli were revealed. In addition, the model developed here is efficient, accurate and ready to extend to any geometry of dental implants. It has potential to be used as a clinical application for instant prediction/evaluation of the performance of dental implants.

Investigation of Bone Growth in Additive-Manufactured Pedicle Screw Implant by Using Ti-6Al-4V and Bioactive Glass Powder Composite.

In this study, we optimized the geometry and composition of additive-manufactured pedicle screws. Metal powders of titanium-aluminum-vanadium (Ti-6Al-4V) were mixed with reactive glass-ceramic biomaterials of bioactive glass (BG) powders. To optimize the geometry of pedicle screws, we applied a novel numerical approach to proposing the optimal shape of the healing chamber to promote biological healing. We examined the geometry and composition effects of pedicle screw implants on the interfacial autologous bone attachment and bone graft incorporation through in vivo studies. The addition of an optimal amount of BG to Ti-6Al-4V leads to a lower elastic modulus of the ceramic-metal composite material, effectively reducing the stress-shielding effects. Pedicle screw implants with optimal shape design and made of the composite material of Ti-6Al-4V doped with BG fabricated through additive manufacturing exhibit greater osseointegration and a more rapid bone volume fraction during the fracture healing process 120 days after implantation, per in vivo studies.

A neural network framework for immediate temperature prediction of surgical hand-held drilling.

BACKGROUND AND OBJECTIVE: Heat generation and associated temperature rise in surgical drilling can cause irreversible tissue damage. It is nearly impossible to provide immediate temperature prediction for a hand-held drilling process since both feed rate and motion vary with time. The objective of this study is to present and test a framework for immediate bone drilling temperature visualization based on a neural network (NN) model and a linear time-invariant (LTI) model. METHODS: In this study, the finite element analysis (FEA) model is used as the ground truth. The NN model is used to predict the location-dependent thermal responses of FEA, while LTI is used to superimpose these responses based on the location history of the heat source. The use of LTI can eliminate the uncertainty of the unlimited possibility in the time domain. To test the framework, two three-dimensional drilling cases are studied, one with a constant drilling feed and straight path and the other with a varying feed and a varying path. RESULTS: The NN model using U-net architecture can achieve the predicted correlation of over 97% with only 1% of the total number of data points. Using the framework with U-net and LTI, both case studies show good agreement in temporal and spatial temperature distributions with the ground truth. The average error near the drilling path is less than 10%. Discrepancies are mainly found near the heat source and the regions near the removed material. CONCLUSIONS: An FEA surrogate model for rapid and accurate prediction of 3D temperature during arbitrary bone drilling is successfully made. The overall error is less than 5% on average in the two case studies. Future improvements include strategies for training data selection and data formating.

Mechanical performance of porous biomimetic intervertebral body fusion devices: an in vitro biomechanical study.

BACKGROUND: Degenerative disc disease is one of the most common ailments severely affecting the quality of life in elderly population. Cervical intervertebral body fusion devices are utilized to provide stability after surgical intervention for cervical pathology. In this study, we design a biomimetic porous spinal cage, and perform mechanical simulations to study its performances following American Society for Testing and Materials International (ASTM) standards before manufacturing to improve design process and decrease cost and consumption of material. METHODS: The biomimetic porous Ti-6Al-4 V interbody fusion devices were manufactured by selective laser melting (laser powder bed fusion: LPBF in ISO/ASTM 52900 standard) and subsequently post-processed by using hot isostatic pressing (HIP). Chemical composition, microstructure and the surface morphology were studied. Finite element analysis and in vitro biomechanical test were performed. FINDINGS: The post heat treatment can optimize its mechanical properties, as the stiffness of the cage decreases to reduce the stress shielding effect between two instrumented bodies. After the HIP treatment, the ductility and the fatigue performance are substantially improved. The use of HIP post-processing can be a necessity to improve the physical properties of customized additive manufacturing processed implants. INTERPRETATION: In conclusion, we have successfully designed a biomimetic porous intervertebral device. HIP post-treatment can improve the bulk material properties, optimize the device with reduced stiffness, decreased stress shielding effect, while still provide appropriate space for bone growth. CLINICAL SIGNIFICANCE: The biomechanical performance of 3-D printed biomimetic porous intervertebral device can be optimized. The ductility and the fatigue performance were substantially improved, the simultaneously decreased stiffness reduces the stress shielding effect between two instrumented bodies; while the biomimetic porous structures provide appropriate space for bone growth, which is important in the patients with osteoporosis.

Mechanically Robust Interface at Metal/Muscovite Quasi van der Waals Epitaxy.

Quasi van der Waals epitaxy is an approach to constructing the combination of 2D and 3D materials. Here, we quantify and discuss the 2D/3D interface structure and the corresponding features in metal/muscovite systems. High-resolution scanning transmission electron microscopy reveals the atomic arrangement at the interface. The theoretical results explain the formation mechanism and predict the mechanical robustness of these metal/muscovite quasi van der Waals epitaxies. The evidence of superior interface quality is delivered according to the outstanding performance of the designed systems in both retention (>105 s) and cycling tests (>105 cycles) through electromechanical measurements. With high-temperature X-ray reciprocal space mapping, the unique anisotropy of thermal expansion is discovered and predicted to sustain the thermal stress with a sizable thermal actuation. A maximum bending curvature of 264 m-1 at 243 °C can be obtained in the silver/muscovite heteroepitaxy. The electrothermal and photothermal methods show a fast response to thermal stress and demonstrate the interface robustness.

Failures of Cu-Cu Joints under Temperature Cycling Tests.

In this study, the failure mechanisms of Cu-Cu joints under thermal cycling were investigated. Two structures of dielectrics (PBO/underfill/PBO and SiO2) were employed to seal the joints. Stress gradients induced in the joints with the different dielectrics were simulated using a finite element method (FEM) and correlated with experimental observations. We found that interfacial voids were forced to move in the direction from high stress regions to low stress ones. The locations of migrated voids varied with the dielectric structures. Under thermal cycling, such voids were likely to move forward to the regions with a small stress change. They relocated and merged with their neighboring voids to lower the interfacial energy.

Enhancement of fatigue resistance by recrystallization and grain growth to eliminate bonding interfaces in Cu-Cu joints.

Cu-Cu joints have been adopted for ultra-high density of packaging for high-end devices. However, cracks may form and propagate along the bonding interfaces during fatigue tests. In this study, Cu-Cu joints were fabricated at 300 °C by bonding 〈111〉-oriented nanotwinned Cu microbumps with 30 µm in diameter. After temperature cycling tests (TCTs) for 1000 cycles, cracks were observed to propagate along the original bonding interface. However, with additional 300 °C-1 h annealing, recrystallization and grain growth took place in the joints and thus the bonding interfaces were eliminated. The fatigue resistance of the Cu-Cu joints is enhanced significantly. Failure analysis shows that cracks propagation was retarded in the Cu joints without the original bonding interface, and the electrical resistance of the joints did not increase even after 1000 cycles of TCT. Finite element analysis was carried to simulate the stress distribution during the TCTs. The results can be correlated to the failure mechanism observed by experimental failure analysis.

Effect of Tin Grain Orientation on Electromigration-Induced Dissolution of Ni Metallization in SnAg Solder Joints.

In this study, symmetrical solder joints (Cu/Ni/SnAg2.3/Ni/Cu) were fabricated. They were electromigration (EM)-stressed at high (8 × 104 A/cm2) or low (1.6 × 104 A/cm2) current densities. Failures in the solder joints with different grain orientations under EM stressing were then characterized. Results show that Ni under-bump-metallurgy (UBM) was quickly dissolved into the solder joints possessing low angles between Sn c-axis and electron direction and massive NiCuSn intermetallic compounds formed in the Sn matrix. The diffusion rate of Ni increased with decreasing orientation grain angle. A theoretical model was also established to analyze the consumption rate of Ni UBM. Good agreement between the modeling and experimental results was obtained. Additionally, we found that voids were more likely to form in the solder joints under high EM stressing.

Artificial intelligence deep learning for 3D IC reliability prediction.

Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the "Good" or "Failure" condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.

Flexible Optogenetic Transducer Device for Remote Neuron Modulation Using Highly Upconversion-Efficient Dendrite-Like Gold Inverse Opaline Structure.

A remote optogenetic device for analyzing freely moving animals has attracted extensive attention in optogenetic engineering. In particular, for peripheral nerve regions, a flexible device is needed to endure the continuous bending movements of these areas. Here, a remote optogenetic optical transducer device made from a gold inverse opaline skeleton grown with a dendrite-like gold nanostructure (D-GIOF) and chemically grafted with upconversion nanoparticles (UCNPs) is developed. This implantable D-GIOF-based transducer device can achieve synergistic interaction of the photonic crystal effect and localized surface plasmon resonance, resulting in considerable UCNP conversion efficiency with a negligible thermal effect under low-intensity 980 nm near-infrared (NIR) light excitation. Furthermore, the D-GIOF-based transducer device exhibits remarkable emission power retention (≈100%) under different bending states, indicating its potential for realizing peripheral nerve stimulation. Finally, the D-GIOF-based transducer device successfully stimulates neuronal activities of the sciatic nerve in mice. This study demonstrates the potential of the implantable device to promote remote NIR stimulation for modulation of neural activity in peripheral nerve regions and provides proof of concept for its in vivo application in optogenetic engineering.

Engineer design process assisted by explainable deep learning network.

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.

4D spatiotemporal modulation of biomolecules distribution in anisotropic corrugated microwrinkles via electrically manipulated microcapsules within hierarchical hydrogel for spinal cord regeneration.

Although traditional 3D scaffolds or biomimetic hydrogels have been used for tissue engineering and regenerative medicine, soft tissue microenvironment usually has a highly anisotropic structure and a dynamically controllable deformation with various biomolecule distribution. In this study, we developed a hierarchical hybrid gelatin methacrylate-microcapsule hydrogel (HGMH) with Neurotrophin-3(NT-3)-loaded PLGA microcapsules to fabricate anisotropic structure with patterned NT-3 distribution (demonstrated as striped and triangular patterns) by dielectrophoresis (DEP). The HGMH provides a dynamic biomimetic sinuate-microwrinkles change with NT-3 spatial gradient and 2-stage time-dependent distribution, which was further simulated using a 3D finite element model. As demonstrated, in comparison with striped-patterned hydrogel, the triangular-patterned HGMH with highly anisotropic array of microcapsules exhibits remarkably spatial NT-3 gradient distributions that can not only guide neural stem cells (NSCs) migration but also facilitate spinal cord injury regeneration. This approach to construct hierarchical 4D hydrogel system via an electromicrofluidic platform demonstrates the potential for building various biomimetic soft scaffolds in vitro tailed to real soft tissues.

A Hybrid Model for Predicting Bone Healing around Dental Implants.

BACKGROUND: The effect of the short-term bone healing process is typically neglected in numerical models of bone remodeling for dental implants. In this study, a hybrid two-step algorithm was proposed to enable a more accurate prediction for the performance of dental implants. METHODS: A mechano-regulation algorithm was firstly used to simulate the tissue differentiation around a dental implant during the short-term bone healing. Then, the result was used as the initial state of the bone remodeling model to simulate the long-term healing of the bones. The algorithm was implemented by a 3D finite element model. RESULTS: The current hybrid model reproduced several features which were discovered in the experiments, such as stress shielding effect, high strength bone connective tissue bands, and marginal bone loss. A reasonable location of bone resorptions and the stability of the dental implant is predicted, compared with those predicted by the conventional bone remodeling model. CONCLUSIONS: The hybrid model developed here predicted bone healing processes around dental implants more accurately. It can be used to study bone healing before implantation surgery and assist in the customization of dental implants.

Novel design of additive manufactured hollow porous implants.

OBJECTIVE: Our aim is to examine the mechanical properties of two types of additive manufactured hollow porous dental implants and 6 and 12-week bone ingrowth after insertion in animals. A 3D numerical model is also developed to show detailed tissue differentiation and to provide design guidelines for implants. METHODS: The two porous and a commercial dental implant were studied by series of in vitro mechanical tests (three-point bending, torsional, screwing torque, and sawbone pull-out tests). They also evaluated by in vivo animal tests (micro-CT analysis) and ex vivo pull-out tests. Moreover, the mechano-regulation algorithm was implemented by the 3D finite element model to predict the history of tissue differentiation around the implants. RESULTS: The results showed that the two porous implants can significantly improve osseointegration after 12-week bone healing. This resulted in good fixation and stability of implants, giving very high maximum pull-out strength 413.1 N and 493.2 N, compared to 245.7 N for the commercial implant. Also, several features were accurately predicted by the mechano-regulation model, such as transversely connected bone formation, and bone resorption occurred in the middle of implants. SIGNIFICANCE: Systematic studies on dental implants with multiple approaches, including new design, mechanical tests, animal tests, and numerical modeling, were performed. Two hollow porous implants significantly improved bone ingrowth compared with commercial implants, while maintaining mechanical strength. Also, the numerical model was verified by animal tests. It improved the efficiency of design and reduce the demand for animal sacrifice.

Giant Resistivity Change of Transparent ZnO/Muscovite Heteroepitaxy.

The piezoresistive effect has shown a remarkable potential for mechanical sensor applications and been sought for its excellent performance. A great attention was paid to the giant piezoresistive effect and sensitivity delivered by silicon-based nanostructures. However, low thermal stability and complicated fabrication process hinder their practical applications. To overcome these issues and enhance the functionalities, we envision the substantial piezopotential in a zinc oxide (ZnO)/muscovite (mica) heteroepitaxy system based on theoretical consideration and realize it in practice. High piezoresistive effect with giant change of resistivity (-80 to 240%) and large gauge factor (>1000) are demonstrated through mechanical bending. The detailed features of heteroepitaxy, electrical transport, and strain are probed to understand the mechanism of such a giant resistivity change. In addition, a bending model is established to reveal the distribution of strain. Finally, we demonstrate a flex sensor featuring high sensitivity, optical transparency, and two-segment sensing with a great potential toward practical applications. Such an oxide heteroepitaxy exhibits excellent piezoresistive properties and mechanical flexibility. In the near future, the importance of flex sensors will emerge because of the precise control in the automation industries, and our results lead to a new design in the field of flex sensors.

Deformations of Ti-6Al-4V additive-manufacturing-induced isotropic and anisotropic columnar structures: Insitu measurements and underlying mechanisms.

The deformations of isotropic and anisotropic Ti-6Al-4V columnar structures fabricated by additive manufacturing were extensively examined. The distinct texture and microstructure distributions were characterised. In situ X-ray diffraction measurements show different lattice activities resulting from the different microstructure distributions. Spatially resolved mapping revealed manufacturing-induced crystallite-orientation distributions that determine the deformation mechanisms. We propose a self-consistent model to correlate the multi-scale characteristics, from the anisotropic-texture-distribution microstructure to the bulk mechanical properties. We determined that basal and pyramidal slip activities were activated by tension deformation. The underlying additive-manufacturing-induced crystal plasticity plays a major role. We find that the texture development of the columnar structures and the distribution of crystallite orientation achieved by different processing conditions during additive manufacturing have important effects on the mechanical properties. The dominant deformation mode for the anisotropic Ti-6Al-4V columnar structure is basal slip, and that for the isotropic Ti-6Al-4V columnar structure is pyramidal slip. The difference may be important for determining the fatigue behaviour.

Control of Dopant Distribution in Yttrium-Doped Bioactive Glass for Selective Internal Radiotherapy Applications Using Spray Pyrolysis.

In this study, we demonstrate the fabrication of Y-doped bioactive glass (BG), which is proposed as a potential material for selective internal radiotherapy applications. Owing to its superior bioactivity and biodegradability, it overcomes the problem of yttrium aluminosilicate spheres that remain in the host body for a long duration after treatment. The preparation of Y-doped BG powders were carried out using a spray pyrolysis method. By using two different yttrium sources, we examine the change of the local distribution of yttrium concentration. In addition, characterizations of phase information, particle morphologies, surface areas, and bioactivity were also performed. The results show that both Y-doped BG powders are bioactive and the local Y distribution can be controlled.

The Analysis of Superelasticity and Microstructural Evolution in NiTi Single Crystals by Molecular Dynamics.

Superelasticity in shape memory alloys is an important feature for actuators and medical devices. However, the function of the devices is typically limited by mechanical bandwidth and fatigue, which are dominated by the microstructures. Thus, in order to correlate the mechanical response and the microstructures, the microstructural evolution in NiTi single crystals under the compression, tensile, and shearing tests is simulated by molecular dynamics (MD) in the current study. Then, the martensite variant identification method, which identifies the crystal variants/phases of each lattice based on the transformation matrix, is used to post-process the MD results. The results with the detailed information of variants and phases reveal many features that have good agreement with those reported in the literature, such as X-interfaces and the transitional orthorhombic phase between the austenite and monoclinic phases. A new twin structure consisting of diamond and wedge-shaped patterns is also discovered. The macroscopic behavior, such as stress-strain curves and the total energy profile, is linked with the distribution of dislocation and twin patterns. The results suggest that the loading cases of shear and compression allow a low critical strain for the onset of martensitic transformation and a better superelasticity behavior. Therefore, the two loading cases are suitable to apply to the NiTi actuators. The current work is expected to provide insight into the mechanical responses and design guideline for NiTi shape memory alloy actuators.