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Abstract Bionic multifunctional structural materials that are lightweight, strong, and perceptible have shown great promise in sports, medicine, and aerospace applications. However, smart monitoring devices with integrated mechanical protection and piezoelectric induction are limited. Herein, we report a strategy to grow the recyclable and healable piezoelectric Rochelle salt crystals in 3D-printed cuttlebone-inspired structures to form a new composite for reinforcement smart monitoring devices. In addition to its remarkable mechanical and piezoelectric performance, the growth mechanisms, the recyclability, the sensitivity, and repairability of the 3D-printed Rochelle salt cuttlebone composite were studied. Furthermore, the versatility of composite has been explored and applied as smart sensor armor for football players and fall alarm knee pads, focusing on incorporated mechanical reinforcement and electrical self-sensing capabilities with data collection of the magnitude and distribution of impact forces, which offers new ideas for the design of next-generation smart monitoring electronics in sports, military, aerospace, and biomedical engineering. 

Abstract For the bottom-up based stereolithography (SL) process, a separation process is required to detach the newly cured layer from the constrained surface in the fabrication process. Excessive separation force will cause damage to the built layers and the constrained surface. Different surface coatings, platform motions including tilting and sliding, and the utilization of oxygen-permeable films have been developed to address the separation-related problems. Among these approaches, the vibration-assisted (VA) separation method to reduce the separation force has limited study. The underlying mechanism of the VA separation-based method remains unexplored, and the best way to use VA separation in the bottom-up based SL process is still unclear. In this paper, a new VA separation design for the SL process is presented. A prototype system was built to study the VA separation mechanism. Experiments on the separation performance under different parameters, including vibration frequency, pre-stress level, and exposure area, were conducted. Based on the collected separation force data, an analytical model based on the mechanics of fatigue fracture was built. The separation behaviors related to different shape size and topology were also studied and compared. The results showed that the separation force in SL was significantly reduced using the VA separation-based method. Furthermore, the relationship between the separation force and the separation time conforms to the stress-based fatigue model. This study also provides insights on how to choose process parameters by considering the trade-offs between separation force and building efficiency. 

Flame-retardant and thermal management structures have attracted great attention due to the requirement of high-temperature exposure in industrial, aerospace, and thermal power fields, but the development of protective fire-retardant structures with complex shapes to fit arbitrary surfaces is still challenging. Herein, we reported a rotation-blade casting-assisted 3D printing process to fabricate nacre-inspired structures with exceptional mechanical and flame-retardant properties, and the related fundamental mechanisms are studied. 3-(Trimethoxysilyl)propyl methacrylate (TMSPMA) modified boron nitride nanoplatelets (BNs) were aligned by rotation-blade casting during the 3D printing process to build the “brick and mortar” architecture. The 3D printed structures are more lightweight, while having higher fracture toughness than the natural nacre, which is attributed to the crack deflection, aligned BN (a-BNs) bridging, and pull-outs reinforced structures by the covalent bonding between TMSPMA grafted a-BNs and polymer matrix. Thermal conductivity is enhanced by 25.5 times compared with pure polymer and 5.8 times of anisotropy due to the interconnection of a-BNs. 3D printed heat-exchange structures with vertically aligned BNs in complex shapes were demonstrated for efficient thermal control of high-power light-emitting diodes. 3D printed helmet and armor with a-BNs show exceptional mechanical and fire-retardant properties, demonstrating integrated mechanical and thermal protection. 

Abstract Motivation Cryoelectron tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at submolecular resolution. Such information is critical for the accurate understanding of cellular processes. However, subtomogram classification remains one of the major challenges for the systematic recognition and recovery of the macromolecule structures in cryo-ET because of imaging limits and data quantity. Recently, deep learning has significantly improved the throughput and accuracy of large-scale subtomogram classification. However, often it is difficult to get enough high-quality annotated subtomogram data for supervised training due to the enormous expense of labeling. To tackle this problem, it is beneficial to utilize another already annotated dataset to assist the training process. However, due to the discrepancy of image intensity distribution between source domain and target domain, the model trained on subtomograms in source domain may perform poorly in predicting subtomogram classes in the target domain. Results In this article, we adapt a few shot domain adaptation method for deep learning-based cross-domain subtomogram classification. The essential idea of our method consists of two parts: (i) take full advantage of the distribution of plentiful unlabeled target domain data, and (ii) exploit the correlation between the whole source domain dataset and few labeled target domain data. Experiments conducted on simulated and real datasets show that our method achieves significant improvement on cross domain subtomogram classification compared with baseline methods. Availability and implementation Software is available online https://github.com/xulabs/aitom. Supplementary information Supplementary data are available at Bioinformatics online. 

Lightweight and strong structural materials attract much attention due to their strategic applications in sports, transportation, aerospace, and biomedical industries. Nacre exhibits high strength and toughness from the brick-and-mortar–like structure. Here, we present a route to build nacre-inspired hierarchical structures with complex three-dimensional (3D) shapes by electrically assisted 3D printing. Graphene nanoplatelets (GNs) are aligned by the electric field (433 V/cm) during 3D printing and act as bricks with the polymer matrix in between as mortar. The 3D-printed nacre with aligned GNs (2 weight %) shows lightweight property (1.06 g/cm 3 ) while exhibiting comparable specific toughness and strength to the natural nacre. In addition, the 3D-printed lightweight smart armor with aligned GNs can sense its damage with a hesitated resistance change. This study highlights interesting possibilities for bioinspired structures, with integrated mechanical reinforcement and electrical self-sensing capabilities for biomedical applications, aerospace engineering, as well as military and sports armors.