Isotropic Network Research Articles

Human Muscle Inspired Anisotropic and Dynamic Metal Ion-Coordinated Mechanically Robust, Stretchable and Swelling-Resistant Hydrogels for Underwater Motion Sensing and Flexible Supercapacitor Application.

Mechanically robust and anisotropic conductive hydrogels have emerged as crucial components in the field of flexible electronic devices, since they possess high mechanical properties and intelligent sensing capabilities. However, the hydrogels often swell on exposure to aqueous medium because of their hydrophilicity, which compromises their mechanical properties. Additionally, the hydrogels' isotropic polymeric networks demonstrate isotropic ion transport, which significantly diminishes the sensing capabilities of electrical devices based on hydrogels. These factors greatly limit their use in flexible and wearable sensors. In this study, we have developed poly(acrylamide-co-maleic acid-co-butyl acrylate) based anisotropic hydrogels by prestretching and drying, followed by ionic cross-linking to fix the alignment. The anisotropic arrangement of the polymer network resulted in significant improvements in mechanical performance and electrical conductivity along the prestretching direction. This anisotropic hydrogel combines hydrophobic and metal ion-ligand interactions, enhancing the maximum tensile strength up to 11 MPa along the prestretching direction, about 3 times higher than in the perpendicular direction. The optimized 200% prestretched hydrogel exhibited high tensile strength (7 MPa), flexibility (fracture strain 370%), high toughness (16 MJ m-3) and antiswelling behavior in water (equilibrium swelling ratio 2% after 15 days). alongside higher conductivity (3 times higher) and strain sensing ability (4 times higher gauge factor) along the prestretching direction. The hydrogel demonstrated efficient and stable underwater sensing for underwater communication and to monitor human limb position and movement. The anisotropic hydrogel electrolyte-based flexible supercapacitor exhibited 117 Fg-1 specific capacitance at 0.5 Ag-1, and maximum energy density 5.85 Whkg-1, significantly higher than the corresponding values for the isotropic hydrogel-based device (88 F g-1 and 4.4 Whkg-1, respectively). This hydrogel mimics the structural design of unidirectionally oriented muscle fibers, showing better direction dependent functional properties than the corresponding isotropic hydrogel. The anti-swelling ability and retention of mechanical and conductive properties of these hydrogels in aqueous environment suggest long-term usage capability of these functional materials.

Strong and Tough Multilayer Heterogeneous Pyrocarbon Based Composites

AbstractThe integration of bio‐inspired hierarchical design into synthetic carbon materials holds promise for developing lightweight, high‐performance structural materials. Hierarchical and heterogeneous nanoscale units are crucial to load bearing and mechanical functionality yet receives limited attention for difficulties in accurate regulation. Herein, one kind of multilayered heterogeneous have proposed pyrocarbon based composite constructed through chemical vapor deposition method with nanoscale graphene orderliness regulation. Sequential construction from orientational graphene sheets to nondirective isotropic networks is achieved through controlling the size and morphology of pyrolysis intermediates with moderate oxidation, being expected to facilitate load transfer and structural stability through dynamically adjustment in heterogeneous interfacial sliding. The resulting multilayer heterogeneous phases exhibit impressive strengthening and toughening mechanisms dominated by microstructural multiscale coupling, which provides new solutions for developing structural composites for high‐temperature aerospace applications. Additionally, the multiple internal reflections arising from the abundant heterogeneous interfaces significantly improve the efficiency of electromagnetic microwave absorption. The multilayered heterogeneous interfaces achieved through orderliness manipulation offer novel insights into the assembly of mechanical functionality integration for wide‐temperature applications and high‐performance macroscopic carbon‐based composites.

Identification of Multiple Events Based on Two-Dimensional Images and Isotropic Network in Optical Fiber Perimeter Security System

Identification of Multiple Events Based on Two-Dimensional Images and Isotropic Network in Optical Fiber Perimeter Security System

Solvent-Mediated Synthesis and Characterization of Li3InCl6 Electrolytes for All-Solid-State Li-Ion Battery Applications.

Superionic halides have attracted widespread attention as solid electrolytes due to their excellent ionic conductivity, soft texture, and stability toward high-voltage electrode materials. Among them, Li3InCl6 has aroused interest since it can be easily synthesized in water or ethanol. However, investigations into the influence of solvents on both the crystal structure and properties remain unexplored. In this work, Li3InCl6 is synthesized by three different solvents: water, ethanol, and water-ethanol mixture, and the difference in properties has been studied. The results show that the product obtained by the ethanol solvent demonstrates the largest unit cell parameters with more vacancies, which tend to crystallize on the (131) plane and provide the 3D isotropic network migration for lithium-ions. Thus, it exhibits the highest ionic conductivity (1.06 mS cm-1) at room temperature and the lowest binding energy (0.272 eV). The assembled all-solid-state lithium metal batteries (ASSLMBs) employing Li3InCl6 electrolytes demonstrate a high initial discharge capacity of 153.9 mA h g-1 at 0.1 C (1 C = 170 mA h g-1) and the reversible capacity retention rate can reach 82.83% after 50 cycles. This work studies the difference in ionic conductivity between Li3InCl6 electrolytes synthesized by different solvents, which can provide a reference for the future synthesis of halide electrolytes and enable their practical application in halide-based ASSLMBs with a high energy density.

An autonomous design algorithm to experimentally realize three-dimensionally isotropic auxetic network structures without compromising density

Auxetic materials have a negative Poisson’s ratio and are of significant interest in applications that include impact mitigation, membrane separations and biomedical engineering. While there are numerous examples of structured materials that exhibit auxetic behavior, the examples of engineered auxetic structures is largely limited to periodic lattice structures that are limited to directional or anisotropic auxetic response. Structures that exhibit a three-dimensionally isotropic auxetic response have been, unfortunately, slow to evolve. Here we introduce an inverse design algorithm based on global node optimization to design three-dimensional auxetic metamaterial structures from disordered networks. After specifying the target Poisson’s ratio for a structure, an inverse design algorithm is used to adjust the positions of all nodes in a disordered network structure until the desired mechanical response is achieved. The proposed algorithm allows independent control of shear and bulk moduli, while preserving the density and connectivity of the networks. When the angle bending stiffness in the network is kept low, it is possible to realize optimized structures with a Poisson’s ratios as low as −0.6. During the optimization, the bulk modulus of these networks decreases by almost two orders of magnitude, but the shear modulus remains largely unaltered. The materials designed in this manner are fabricated by dual-material 3D-printing, and are found to exhibit the mechanical responses that were originally encoded in the computational design engine. The approach proposed here provides a materials-by-design platform that could be extended for engineering of optical, acoustic, and electrical properties, beyond the design of auxetic metamaterials.

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins.

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

Huo Cao: a unique collectively utilized natural textile material and its structure and properties

Huo Cao, scientifically known as Gerbera delavayi Franch, is a perennial herb that exhibits the spontaneous growth of fibers, which intricately interweave to form a white network on the underside of its leaf. The Huo Cao fiber network demonstrated distinctiveness by being able to be twisted straight into yarns. The textiles made by Huo Cao also have their own unique characteristics. The structure and characteristics of the Huo Cao fiber and its network were examined in this study using surface morphology, SEM, chemical composition, TGA, FTIR, wettability, XRD, and chemical resistance tests. The outcomes were compared with those of cotton. The Huo Cao fiber naturally forms an isotropic fiber network. When comparing fiber to cotton, it is evident that they have a comparable physical structure. However, there are significant differences in terms of cellulose content and crystallinity. Huo Cao fiber is rich in flavonoids, which possess antibacterial properties. The Huo Cao network exhibits an average contact angle of 140°. Huo Cao fiber is susceptible only to powerful acids and is compatible with the skin.

DNA Cryogels with Anisotropic Mechanical and Responsive Properties for Specific Cell Capture and Release.

Due to their programmable stimuli-responsiveness, excellent biocompatibility, and water-rich and soft structures similar to biological tissues, smart DNA hydrogels hold great promise for biosensing and biomedical applications. However, most DNA hydrogels developed to date are composed of randomly oriented and isotropic polymer networks, and the resulting slow response to biotargets and lack of anisotropic properties similar to those of biological tissues have limited their extensive applications. Herein, anisotropic DNA hydrogels consisting of unidirectional void channels internally oriented up to macroscopic length scales were constructed by a directional cryopolymerization method, as exemplified by a DNA-incorporated covalently cross-linked DNA cryogel and a DNA duplex structure noncovalently cross-linked DNA cryogel. Results showed that the formation of unidirectional channels significantly improved the responsiveness of the gel matrix to biomacromolecular substances and further endowed the DNA cryogels with anisotropic properties, including anisotropic mechanical properties, anisotropic swelling/shrinking behaviors, and anisotropic responsiveness to specific biotargets. Moreover, the abundant oriented and long macroporous channels in the gel matrix facilitated the migration of cells, and through the introduction of aptamer structures and thermosensitive polymers, an anisotropic DNA cryogel-based platform was further constructed to achieve the highly efficient capture and release of specific cells. These anisotropic DNA hydrogels may provide new opportunities for the development of anisotropic separation and biosensing systems.

Electron Beam-Modified Collagen Type I Fibers: Synthesis and Characterization of Mechanical Response.

Ten MeV electron beam treatment facilitates a biomimetic introduction of cross-links in collagenous biopolymer systems, modifying their viscoelastic properties, mechanical stability, and swelling behavior. For reconstituted collagen type I fibers, electron-induced cross-linking opens up new perspectives regarding future biomedical applications in terms of tissue and ligament engineering. We demonstrate how electron irradiation affects stiffness both in low-strain regimes and in postyield regimes of biocompatible reconstituted rat tail collagen type I fibers. Stress-strain tests show a dose-dependent increase in modulus in the nonlinear elastic response, indicating a central role of induced cross-links in mechanical stability. Environmental scanning electron microscopy after fiber rupture reveals aligned distributed collagen fibril domains under the fiber surface for as-prepared fibers, accompanied by a ductile fracture behavior, whereas, in tensile tests imaged by light microscopy after 10 MeV electron treatment, isotropic network topologies are observed until the occurrence of a brittle type of rupture. Based on the biomimicry of the process, these findings might pave the way for a novel type of synthesis of tailored tendon or ligament substitutes.

A Science Friction Story: Molecular Interactions in Semiflexible Polymer Networks

AbstractEstablished model theories, developed to capture the mechanical behavior of soft, complex materials composed of semiflexible polymers, assume that entropic interactions between filaments are primarily responsible for determining the mechanical response. In recent studies, the generally accepted tube model has been challenged in terms of this basic assumption about filament–filament interactions, but also because of its predictions regarding the frequency dependence of the elastic modulus in the intermediate frequency regime. A central question is how molecular interactions and friction between network constituents influence the rheological response of isotropic entangled networks of semiflexible polymers. It has been previously shown that friction forces between aligned pairs of actin filaments are not negligible. Here, the influence of friction forces and attractive interactions on network rheology is systematically investigated by means of targeted surface modification. It is shown that these forces have a qualitative and quantitative influence on the viscoelastic properties of semiflexible polymer networks and contribute to their response to nonlinear deformations. By comparing two polymer model systems with respect to their surface compositions, a possible explanation is given about the origin of acting forces on a molecular level.

Local stability of traffic equilibria in an isotropic network

For a static economic model of auto traffic in an isotropic zone, this paper classifies possible equilibria into three types, by whether traffic is hypercongested and by the relative slopes of “supply” and “demand” curves. We then conduct a local stability analysis of each type when density, demand and the unit travel time (inverse speed) evolve gradually and simultaneously according to dynamical systems of differential equations. Some hypercongested equilibria may be stable when demand adjusts quickly enough to congestion. Other hypercongested equilibria, which have counterintuitive comparative statics, are never stable. Non-hypercongested equilibria can be unstable under special circumstances. A discrete event simulation with a dynamic Poisson arrival process supports the results of the formal analysis.

Lightweight Isotropic Convolutional Neural Network for Plant Disease Identification

In today’s world, agricultural products are becoming increasingly scarce globally due to a variety of factors, and the early and accurate automatic identification of plant diseases can help ensure the stability and sustainability of agricultural production, improve the quality and safety of agricultural products, and help promote agricultural modernization and sustainable development. For this purpose, a lightweight deep isotropic convolutional neural network model, FoldNet, is designed for plant disease identification in this study. The model improves the architecture of residual neural networks by first folding the chain of the same blocks and then connecting these blocks with jump connections of different distances. Such a design allows the neural network to explore a larger receptive domain, enhancing its multiscale representation capability, increasing the direct propagation of information throughout the network, and improving the performance of the neural network. The FoldNet model achieved a recognition accuracy of 99.84% on the laboratory dataset PlantVillage using only 685k parameters and a recognition accuracy of 90.49% on the realistic scene dataset FGVC8 using only 516k parameters, which is competitive with other state-of-the-art models. In addition, as far as we know, our model is the first model that has fewer than 1M parameters while achieving state-of-the-art accuracy in plant disease identification. This proposal facilitates precision agriculture applications on mobile, low-end terminals.

Neural networks meet hyperelasticity: A guide to enforcing physics

In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common constitutive conditions by construction, and in particular, is applicable to compressible material behavior. Using different sets of invariants as inputs, a hyperelastic potential is formulated as a convex neural network, thus fulfilling symmetry of the stress tensor, objectivity, material symmetry, polyconvexity, and thermodynamic consistency. In addition, a physically sensible stress behavior of the model is ensured by using analytical growth terms, as well as normalization terms which ensure the undeformed state to be stress free and with zero energy. In particular, polyconvex, invariant-based stress normalization terms are formulated for both isotropic and transversely isotropic material behavior. By fulfilling all of these conditions in an exact way, the proposed physics-augmented model combines a sound mechanical basis with the extraordinary flexibility that neural networks offer. Thus, it harmonizes the theory of hyperelasticity developed in the last decades with the up-to-date techniques of machine learning. Furthermore, the non-negativity of the hyperelastic neural network-based potentials is numerically examined by sampling the space of admissible deformations states, which, to the best of the authors’ knowledge, is the only possibility for the considered nonlinear compressible models. For the isotropic neural network model, the sampling space required for that is reduced by analytical considerations. In addition, a proof for the non-negativity of the compressible Neo-Hooke potential is presented. The applicability of the model is demonstrated by calibrating it on data generated with analytical potentials, which is followed by an application of the model to finite element simulations. In addition, an adaption of the model to noisy data is shown and its extrapolation capability is compared to models with reduced physical background. Within all numerical examples, excellent and physically meaningful predictions have been achieved with the proposed physics-augmented neural network.

Constitutive modelling of fibre networks with stretch distributions, Part II: Alternative representation, affine distribution and anisotropy

In this paper, the concept for modelling materials with fibre network microstructures introduced in Part I of this series is reconsidered. At first, an alternative representation of the theory is provided that entails advantages in terms of numerical implementation. Next, the new constitutive approach is applied to affine networks, whose fibre stretches are distributed according to the affine distribution. Despite the tremendously widespread use of this model, it seems that the general form of the corresponding distribution has remained largely unexplored to date. The thus obtained reformulation of the affine full network model provides deep insight into this concept, and may help overcoming well-known numerical problems with the associated spherical integration, e.g. when highly non-linear or piece-wise defined fibre laws are used. The latter case is typical for applications in biomechanics, where fibres are frequently assumed to have negligible compressive resistance. While the developments of our theory thus far had focused on isotropic networks, we here showcase for the affine case how the anisotropy caused by non-uniform directional distributions of the fibres can be incorporated in the novel approach. Finally, it is shown that several earlier approaches to model networks of affinely deforming fibres or polymer chains result as special cases of our theory.

A New Spinel Chloride Solid Electrolyte with High Ionic Conductivity and Stability for Na-Ion Batteries

Halide materials are of current interest as solid electrolytes for all-solid-state sodium-ion batteries (ASIBs), due to their good balance between ionic conductivity and electrochemical stability. In this work, by using density functional theory combined with deep potential model and grand potential phase diagram analysis, we report a new spinel chloride (Na2Y2/3Cl4) and systemically evaluate its potential for the solid electrolyte. The spinel Na2Y2/3Cl4 exhibits a high ionic conductivity of 0.94 mS/cm at room temperature and has a three-dimensional isotropic diffusion network comprised of face-sharing octahedra and tetrahedra. Further analysis of the diffusion mechanism indicates that the Na+ conductivity mainly derives from Na ions in the 8a site while the Na ions in the 16d site are mainly used for forming the rhombus skeleton. Besides, the spinel Na2Y2/3Cl4 has a wide electrochemical window of 0.59–3.76 V and good interfacial stability with high-voltage cathodes, which ensures its ability to improve the energy density of ASIBs. This study demonstrates the promising application of the spinel framework in sodium solid electrolytes and sheds new light on developing the halide-based solid electrolyte for ASIBs.

Bioprinting of structurally organized meniscal tissue within anisotropic melt electrowritten scaffolds.

The meniscus is characterised by an anisotropic collagen fibre network which is integral to its biomechanical functionality. The engineering of structurally organized meniscal grafts that mimic the anisotropy of the native tissue remains a significant challenge. In this study, inkjet bioprinting was used to deposit a cell-laden bioink into additively manufactured scaffolds of differing architectures to engineer fibrocartilage grafts with user defined collagen architectures. Polymeric scaffolds consisting of guiding fibre networks with varying aspect ratios (1:1; 1:4; 1:16) were produced using either fused deposition modelling (FDM) or melt electrowriting (MEW), resulting in scaffolds with different internal architectures and fibre diameters. Scaffold architecture was found to influence the spatial organization of the collagen network laid down by the jetted cells, with higher aspect ratios (1:4 and 1:16) supporting the formation of structurally anisotropic tissues. The MEW scaffolds supported the development of a fibrocartilaginous tissue with compressive mechanical properties similar to that of native meniscus, while the anisotropic tensile properties of these constructs could be tuned by altering the fibre network aspect ratio. This MEW framework was then used to generate scaffolds with spatially distinct fibre patterns, which in turn supported the development of heterogenous tissues consisting of isotropic and anisotropic collagen networks. Such bioprinted tissues could potentially form the basis of new treatment options for damaged and diseased meniscal tissue. STATEMENT OF SIGNIFICANCE: This study describes a multiple tool biofabrication strategy which enables the engineering of spatially organized fibrocartilage tissues. The architecture of MEW scaffolds can be tailored to not only modulate the directionality of the collagen fibres laid down by cells, but also to tune the anisotropic tensile mechanical properties of the resulting constructs, thereby enabling the engineering of biomimetic meniscal-like tissues. Furthermore, the inherent flexibility of MEW enables the development of zonally defined and potentially patient-specific implants.

The Incoherence of Deep Isotropic Neural Networks Increases Their Performance in Image Classification

Although neural-network architectures are critical for their performance, how the structural characteristics of a neural network affect its performance has still not been fully explored. Here, we map architectures of neural networks to directed acyclic graphs (DAGs), and find that incoherence, a structural characteristic to measure the order of DAGs, is a good indicator for the performance of corresponding neural networks. Therefore, we propose a deep isotropic neural-network architecture by folding a chain of the same blocks and then connecting the blocks with skip connections at different distances. Our model, named FoldNet, has two distinguishing features compared with traditional residual neural networks. First, the distances between block pairs connected by skip connections increase from always equal to one to specially selected different values, which lead to more incoherent graphs and let the neural network explore larger receptive fields and, thus, enhance its multi-scale representation ability. Second, the number of direct paths increases from one to multiple, which leads to a larger proportion of shorter paths and, thus, improves the direct propagation of information throughout the entire network. Image-classification results on CIFAR-10 and Tiny ImageNet benchmarks suggested that our new network architecture performs better than traditional residual neural networks. FoldNet with 25.4M parameters can achieve 72.67% top-1 accuracy on the Tiny ImageNet after 100 epochs, which is competitive compared with the-state-of-art results on the Tiny ImageNet.

In vitro assembly, positioning and contraction of a division ring in minimal cells

Constructing a minimal machinery for autonomous self-division of synthetic cells is a major goal of bottom-up synthetic biology. One paradigm has been the E. coli divisome, with the MinCDE protein system guiding assembly and positioning of a presumably contractile ring based on FtsZ and its membrane adaptor FtsA. Here, we demonstrate the full in vitro reconstitution of this machinery consisting of five proteins within lipid vesicles, allowing to observe the following sequence of events in real time: 1) Assembly of an isotropic filamentous FtsZ network, 2) its condensation into a ring-like structure, along with pole-to-pole mode selection of Min oscillations resulting in equatorial positioning, and 3) onset of ring constriction, deforming the vesicles from spherical shape. Besides demonstrating these essential features, we highlight the importance of decisive experimental factors, such as macromolecular crowding. Our results provide an exceptional showcase of the emergence of cell division in a minimal system, and may represent a step towards developing a synthetic cell.

Hyperelastic continuum models for isotropic athermal fibrous networks.

Many biological materials contain fibrous protein networks as their main structural components. Understanding the mechanical properties of such networks is important for creating biomimicking materials for cell and tissue engineering, and for developing novel tools for detecting and diagnosing disease. In this work, we develop continuum models for isotropic, athermal fibrous networks by combining a single-fibre model that describes the axial response of individual fibres, with network models that assemble individual fibre properties into overall network behaviour. In particular, we consider four different network models, including the affine, three-chain, eight-chain, and micro-sphere models, which employ different assumptions about network structure and kinematics. We systematically investigate the ability of these models to describe the mechanical response of athermal collagen and fibrin networks by comparing model predictions with experimental data. We test how each model captures network behaviour under three different loading conditions: uniaxial tension, simple shear, and combined tension and shear. We find that the affine and three-chain models can accurately describe both the axial and shear behaviour, whereas the eight-chain and micro-sphere models fail to capture the shear response, leading to unphysical zero shear moduli at infinitesimal strains. Our study is the first to systematically investigate the applicability of popular network models for describing the macroscopic behaviour of athermal fibrous networks, offering insights for selecting efficient models that can be used for large-scale, finite-element simulations of athermal networks.

Strong tough hydrogel solar evaporator with wood skeleton construction enabling ultra‐durable brine desalination

AbstractHydrogels, a highly versatile material platform, are poised to become a promising candidate for interfacial solar evaporators on account of their record‐high evaporation rates. Nevertheless, conventional hydrogels with loose crosslinking, low solid content, and isotropic network structure are plagued by fragile mechanical tolerance, impairing their application in severe seawater environments. Herein, we develop a skeleton‐construct polyelectrolyte hydrogel (SCPH) evaporator by a universal, simple strategy that maintains not only the original properties of hydrogels, but also endows a high mechanical strength. By virtue of this strategy, the SCPH increased its mechanical strength to 1 MPa, 500 times higher than the original hydrogel. Surprisingly, the strategy also endows the SCPH with some extra properties, such as a high salt rejection ratio, a high water‐pumping rate, and low thermal conductivity, which render the SCPH a perfect evaporator. Specifically, this SCPH material significantly improves the long‐term stability of evaporators, enabling the evaporator to be capable of evaporating for 30 days in 20 wt% brine at a stable evaporation rate, exceeding the longest evaporation time ever reported. In addition, it achieves an evaporation rate of up to 2.13 kg m−2 h−1, which is twice that of the currently existing polyelectrolyte hydrogel evaporators. The skeleton construction has the potential to significantly improve the efficiency, stability, and durability of evaporators, hence bringing interfacial desalination technology toward practical applications.image