This work presents a comparative study on the reliability of auxetic (re-entrant honeycomb) and non-auxetic (diamond lattice and conventional honeycomb) lattice composites. The analyzed specimen ...consists of two unidirectional carbon fiber reinforced composite (CFRP) face sheets and a 3D-printed polymeric core. Low velocity impact tests are conducted first to characterize the unit cell deformation pattern, and we further explore its influence on core structure behavior as well as sandwich panel performance. It is found that the re-entrant topology exhibits lower energy absorption capacity but superior robustness and durability. Consequently, the re-entrant panel performs best in both force mitigation and energy dissipation, provided that the impact energy is appropriate. Furthermore, employing re-entrant core not only stabilizes the occurrences of the face sheet penetration as the impact energy increases, but also grants the sandwich panel consistent behaviors under multi-cycle impacts. These unique performances are due to the global instability of the auxetic structure, which yields more compliant deformation and less stress concentration. Resultant discrepancies shall be interpreted with the sandwich core deformation for validation. These findings pave the way for developing new class of auxetic lattice composites, especially under cyclic loading conditions, through a combination of rational design and 3D printing.
Display omitted
•Auxetic core exhibits lower energy absorption capacity but superior robustness and durability compared with non-auxetic design.•Only if the impact energy is appropriate will the auxetic panel yield best performance in mitigating impact load.•The robust auxetic core shows same deformation pattern under various impact energy, and thus stabilizes face sheet penetration occurrence.•The durable auxetic core remains intact after multi-cycle impacts, and correspondingly grants lattice composites consistent performance.
There have been significant advances in our understanding of human autoimmunity that have led to improvements in classification and diagnosis and, most importantly, research advances in new ...therapies. The importance of autoimmunity and the mechanisms that lead to clinical disease were first recognized about 50 years ago following the pioneering studies of Macfarlane Burnett and his Nobel Prize‐winning hypothesis of the ‘forbidden clone’. Such pioneering efforts led to a better understanding not only of autoimmunity, but also of lymphoid cell development, thymic education, apoptosis and deletion of autoreactive cells. Contemporary theories suggest that the development of an autoimmune disease requires a genetic predisposition and environmental factors that trigger the immune pathways that lead, ultimately, to tissue destruction. Despite extensive research, there are no genetic tools that can be used clinically to predict the risk of autoimmune disease. Indeed, the concordance of autoimmune disease in identical twins is 12–67%, highlighting not only a role for environmental factors, but also the potential importance of stochastic or epigenetic phenomena. On the other hand, the identification of cytokines and chemokines, and their cognate receptors, has led to novel therapies that block pathological inflammatory responses within the target organ and have greatly improved the therapeutic effect in patients with autoimmune disease, particularly rheumatoid arthritis. Further advances involving the use of multiplex platforms for diagnosis and identification of new therapeutic agents should lead to major breakthroughs within the next decade.
Two-dimensional (2D) semiconductor nanomaterials hold great promises for future electronics and optics. In this paper, a 2D nanosheets of ultrathin GaSe has been prepared by using mechanical cleavage ...and solvent exfoliation method. Single- and few-layer GaSe nanosheets are exfoliated on an SiO2/Si substrate and characterized by atomic force microscopy and Raman spectroscopy. Ultrathin GaSe-based photodetector shows a fast response of 0.02 s, high responsivity of 2.8 AW–1 and high external quantum efficiency of 1367% at 254 nm, indicating that the two-dimensional nanostructure of GaSe is a new promising material for high performance photodetectors.
Histone H3K4me1/2 methyltransferases MLL3/MLL4 and H3K27 acetyltransferases CBP/p300 are major enhancer epigenomic writers. To understand how these epigenomic writers orchestrate enhancer landscapes ...in cell differentiation, we have profiled genomic binding of MLL4, CBP, lineage-determining transcription factors (EBF2, C/EBPβ, C/EBPα, PPARγ), coactivator MED1, RNA polymerase II, as well as epigenome (H3K4me1/2/3, H3K9me2, H3K27me3, H3K36me3, H3K27ac), transcriptome and chromatin opening during adipogenesis of immortalized preadipocytes derived from mouse brown adipose tissue (BAT). We show that MLL4 and CBP drive the dynamic enhancer epigenome, which correlates with the dynamic transcriptome. MLL3/MLL4 are required for CBP/p300 binding on enhancers activated during adipogenesis. Further, MLL4 and CBP identify super-enhancers (SEs) of adipogenesis and that MLL3/MLL4 are required for SE formation. Finally, in brown adipocytes differentiated in culture, MLL4 identifies primed SEs of genes fully activated in BAT such as Ucp1. Comparison of MLL4-defined SEs in brown and white adipogenesis identifies brown-specific SE-associated genes that could be involved in BAT functions. These results establish MLL3/MLL4 and CBP/p300 as master enhancer epigenomic writers and suggest that enhancer-priming by MLL3/MLL4 followed by enhancer-activation by CBP/p300 sequentially shape dynamic enhancer landscapes during cell differentiation. Our data also provide a rich resource for understanding epigenomic regulation of brown adipogenesis.
Interpenetrating phase composite (IPC), also known as co-continuous composite, is one type of material that may exhibit an unusual combination of high stiffness, strength, energy absorption, and ...damage tolerance. Here we experimentally demonstrate that IPCs fabricated by 3D printing technique with rationally designed architectures can exhibit a fracture toughness 16 times higher than that of conventionally structured composites. The toughening mechanisms arise from the crack-bridging, process zone formation and crack-deflection, which are intrinsically controlled by the rationally designed interpenetrating architectures. We further show that the prominently enhanced fracture toughness in the architected IPCs can be tuned by tailoring the stiffness contrasts between the two compositions. The findings presented here not only quantify the fracture behavior of complex architected IPCs but also demonstrate the potential to achieve tailorable mechanical properties through the integrative rational design and the state-of-the-art advanced manufacturing technique.
Abstract
Metamaterials have demonstrated great potential for controlling wave propagation since they are flexibly adjustable. A new one-dimensional metamaterial model with both a negative effective ...moment of inertia and negative effective stiffness is proposed. A negative effective moment of inertia and negative effective stiffness can be achieved by adjusting the structural parameters in certain frequency ranges. Bandgaps in the low-frequency range with exponential wave attenuation can be generated in the metamaterial. A flat band is obtained that couples two Bragg bandgaps to achieve a wide bandgap in the low-frequency range, where the effective moment of inertia and effective stiffness are both infinite. A zero-frequency bandgap can be achieved by adjusting the structural parameters. Quick attenuation of wave is observed in the zero-frequency ranges with single-negative parameters. Furthermore, an ultrawide-zero-frequency bandgap is obtained by optimizing the structural parameters of the system. In addition, it is easy to switch between the Bragg and locally resonant bandgaps. This new metamaterial can be applied to ultralow-frequency-vibration isolation.
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein ...interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
PDF
