4D Printing of Ultrastretchable Magnetoactive Soft Material Architectures for Soft Actuators.

Magnetoactive soft materials (MSMs) comprising magnetic particles and soft matrices have emerged as smart materials for realizing soft actuators. 4D printing, which involves fabricating 3D architectures that can transform shapes under external magnetic fields, is an effective way to fabricate MSMs-based soft actuators with complex shapes. The printed MSMs must be flexible, stretchable, and adaptable in their magnetization profiles to maximize the degrees of freedom for shape morphing. This study utilizes a facile 4D printing strategy for producing ultrastretchable (stretchability > 1000%) MSM 3D architectures for soft-actuator applications. The strategy involves two sequential steps: (i) direct ink writing (DIW) of the MSM 3D architectures with ink composed of NdFeB and styrene-isoprene block copolymers (SIS) at room temperature and (ii) programming and reconfiguration of the magnetization profiles of the printed architecture using an origami-inspired magnetization method (magnetization field, Hm = 2.7 T). Various differently shaped MSM 3D architectures, which can be transformed into desired shapes under an actuation magnetic field (Ba = 85 mT), are successfully fabricated. In addition, two different soft-actuator applications are demonstrated: a multifinger magnetic soft gripper and a Kirigami-shaped 3D electrical switch with conductive and magnetic functionalities. Our strategy shows potential for realizing multifunctional, shape-morphing, and reprogrammable magnetoactive devices for advanced soft-actuator applications.

Hybrid solid mesh structure for electron beam melting customized implant to treat bone cancer.

15Bone replacement implants manufactured by electron beam melting have been widely studied for use in bone tumor treatment. In this application, a hybrid structure implant with a combination of solid and lattice structures guarantees strong adhesion between bone and soft tissues. This hybrid implant must exhibit adequate mechanical performance so as to satisfy the safety criteria considering repeated weight loading during the patient's lifetime. With a low volume of a clinical case, various shape and volume combinations, including both solid and lattice structures, should be evaluated to provide guidelines for implant design. This study examined the mechanical performance of the hybrid lattice by investigating two shapes of the hybrid implant and volume fractions of the solid and lattice structures, along with microstructural, mechanical, and computational analyses. These results demonstrate how hybrid implants may be designed to improve clinical outcomes by using patient-specific orthopedic implants with optimized volume fraction of the lattice structure, allowing for effective enhancement of mechanical performance as well as optimized design for bone cell ingrowth.

Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.

One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.

Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures.

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the introduction of artificial intelligence (AI) to diagnose fractures, the accuracy of foot fracture diagnosis is lower than that of conventional methods. We developed an AI assistant system that assists with consistent diagnosis and helps interns or non-experts improve their diagnosis of foot fractures, and compared the effectiveness of the AI assistance on various groups with different proficiency. Contrast-limited adaptive histogram equalization was used to improve the visibility of original radiographs and data augmentation was applied to prevent overfitting. Preprocessed radiographs were fed to an ensemble model of a transfer learning-based convolutional neural network (CNN) that was developed for foot fracture detection with three models: InceptionResNetV2, MobilenetV1, and ResNet152V2. After training the model, score class activation mapping was applied to visualize the fracture based on the model prediction. The prediction result was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC), and the F1-Score. Regarding the test set, the ensemble model exhibited better classification ability (F1-Score: 0.837, AUC: 0.95, Accuracy: 86.1%) than other single models that showed an accuracy of 82.4%. With AI assistance for the orthopedic fellow, resident, intern, and student group, the accuracy of each group improved by 3.75%, 7.25%, 6.25%, and 7% respectively and diagnosis time was reduced by 21.9%, 14.7%, 24.4%, and 34.6% respectively.

Meniscus-Guided Micro-Printing of Prussian Blue for Smart Electrochromic Display.

Using energy-saving electrochromic (EC) displays in smart devices for augmented reality makes cost-effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal-organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro-patterning. This work presents a novel micro-printing strategy for PB patterns using localized crystallization of FeFe(CN)6 on a substrate confined by the acidic-ferric-ferricyanide ink meniscus, followed by thermal reduction at 120 °C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl3 ·K3 Fe(CN)6 , printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: -0.2-0.5 V) with 200 CV cycles. The PB-based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro-patterns could be used for advanced EC displays and various functional devices.

Correlation Between Microstructure and Tensile Properties of STS 316L and Inconel 718 Fabricated by Selective Laser Melting (SLM).

The correlation between microstructure and tensile properties of selective laser melting (SLM) processed STS 316L and Inconel 718 were investigated at various heights (top, middle and bottom) and planes (YZ, ZX and XY). Columnar grains and dendrites were formed by directional growth during solidification. The average melt pool width and depth, and scan track width were similar in both specimens due to fixed processing parameters. SLM Inconel 718 has moderate tensile strength (1165 MPa) and tensile elongation (11.5%), whereas SLM STS 316L has outstanding tensile strength (656 MPa) and tensile elongation (75%) compared to other SLM processed STS 316L. Fine columnar diameter (0.5 µm) and dense microstructures (porosity: 0.35%) in SLM STS 316L promoted the enhancement of tensile elongation by suitable processing condition. Fractographic analysis suggested that the lack of fusion pore with unmelted powder should be avoided to increase tensile properties by controlling processing parameters.

Microstructural Evolution of Al-Zn-Mg-Cu Alloys in Accordance with Homogenization Time.

The microstructural evolution of Al-Zn-Mg-Cu alloys has been investigated for the homogenization time effect on the texture, grain orientation and dislocation density. The Al-Zn-Mg-Cu alloys were casted and homogenized for 4, 8, 16 and 24 hours. Electron backscatter diffraction (EBSD) analysis was conducted to characterize the microstructural behavior. Micropillars were fabricated using focused ion beam (FIB) milling in grains of specific crystallographic orientations. Coarse precipitations in the grain boundaries are S (Al2CuMg) and T (Al2Mg3Zn3) phases verified by scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) observation. With increasing homogenization time, equiaxed cell sizes increased. The volume fraction of S and T phases decreased with the diffusion of atomic elements into matrix. The Vickers hardness and tensile strength values decreased with homogenization temperature. The micropillar compression analysis was compared to macro tensile test results to understand the size effect and strain burst phenomenon on the mechanical properties of Al-Zn-Mg-Cu alloys.

Selective Ion Sweeping on Prussian Blue Analogue Nanoparticles and Activated Carbon for Electrochemical Kinetic Energy Harvesting.

Kinetic energy is an ideal energy source for powering wearable devices or internet of things (IoTs) because of its abundant availability. Currently, most kinetic energy harvesting systems are based on friction or deformation, which require high-frequency motion or high material durability for sustainable energy harvesting. Here, we introduce selective ion sweeping in a hybrid cell consisting of an ion-adsorbing activated carbon and an ion-hosting Prussian blue analogue nanoparticle for electrochemical kinetic energy harvesting. The flow of electrolyte induced by kinetic motion of the cell causes ion sweeping only on the surface of the supercapacitor and induces current flow between the supercapacitor and the battery electrode. This method exhibits 24.9 µW cm-2 as maximum power of system with 34 Ω load in half-cell test, which is several thousand times smaller than the load used in conventional methods. In a long-term test with full cell, this method supplies a continuous current flow ∼6 µA cm-2 at the flow of 40 mL min-1 for 500 cycles without performance decay. The prototype of the hybrid cell demonstrated kinetic energy harvesting from bare hand press with the various flow speeds from 0.41 to 1.39 cm s-1 as well as walking, running, and door closing, which are representative examples of low-frequency kinetic motions in daily life. We believe that the simple structure of the hybrid cell will enable power supply to various applications from miniaturized systems (e.g., IoTs and wearables) to large-scale systems (e.g., ocean wave energy harvesting).