Elastic Networks Research Articles

Prestrain-induced contraction in one-dimensional random elastic chains

Prestrained elastic networks arise in a number of biological and technological systems ranging from the cytoskeleton of cells to tensegrity structures. Motivated by this observation, we here consider a minimal model in one dimension to set the stage for understanding the response of such networks as a function of the prestrain. To this end we consider a chain [one-dimensional (1D) network] of elastic springs upon which a random, zero mean, finite variance prestrain is imposed. Numerical simulations and analytical predictions quantify the magnitude of the contraction as a function of the variance of the prestrain, and show that the chain always shrinks. To test these predictions, we vary the topology of the chain, consider more complex connectivity and show that our results are relatively robust to these changes.

Highly elastic energy storage device based on intrinsically super-stretchable polymer lithium-ion conductor with high conductivity

Stretchable power sources, especially stretchable lithium-ion batteries (LIBs), have attracted increasing attention due to their enormous prospects for powering flexible/wearable electronics. Despite recent advances, it is still challenging to develop ultra-stretchable LIBs that can withstand large deformation. In particular, stretchable LIBs require an elastic electrolyte as a basic component, while the conductivity of most elastic electrolytes drops sharply during deformation, especially during large deformations. This is why highly stretchable LIBs have not yet been realized until now. As a proof of concept, a super-stretchable LIB with strain up to 1200% is created based on an intrinsically super-stretchable polymer electrolyte as the lithium-ion conductor. The super-stretchable conductive system is constructed by an effective diblock copolymerization strategy via photocuring of vinyl functionalized 2-ureido-4-pyrimidone (VFUpy), an acrylic monomer containing succinonitrile and a lithium salt, achieving high ionic conductivity (3.5 × 10−4 mS cm−1 at room temperature (RT)) and large deformation (the strain can reach 4560%). The acrylic elastomer containing Li-ion conductive domains can strongly increase the compatibility between the neighboring elastic networks, resulting in high ionic conductivity under ultra-large deformation, while VFUpy increases elasticity modulus (over three times) and electrochemical stability (voltage window reaches 5.3 V) of the prepared polymer conductor. At a strain of up to 1200%, the resulting stretchable LIBs are still sufficient to power LEDs. This study sheds light on the design and development of high-performance intrinsically super-stretchable materials for the advancement of highly elastic energy storage devices for powering flexible/wearable electronics that can endure large deformation.

Emergence of bilayer-locked states and synthesis of elastic wave networks in a programmable 3D topological metamaterial

Recently, concepts from topological physics have been used to achieve exceptional elastic wave transport phenomena in 3D mechanical systems. Although interesting, the previously studied 3D topological elastic structures are fixed after fabrication and thus cannot adapt to changes in the external environment or operating conditions. This lack of reconfigurability limits potential performance and functionality. In this Letter, we advance the state of the art by proposing a programmable 3D topological metamaterial that can be tuned adaptively via carefully designed metastability. A metastable switching methodology is developed that enables the synthesis of multiple unique topological states in a 3D mechanical structure. By taking advantage of the adaptivity of the proposed metamaterial, the path of 2D topological surface states is on-demand controlled, a bilayer-locked topological state is discovered, and 3D elastic wave networks with advanced filtering and splitting capabilities are realized. The findings presented in this Letter offer insight into how topology can be used to control the flow of energy in 3D elastic structures. Furthermore, the advanced functionalities of the proposed metamaterial could be harnessed to create intelligent and robust devices for various purposes, such as mechanologic, vibration mitigation, energy harvesting, and remote sensing.

Ultraviolet radiation-induced degradation of dermal extracellular matrix and protection by green tea catechins: a randomized controlled trial.

BackgroundLoss and remodelling of the dermal extracellular matrix (ECM) are key features of photodamaged human skin. Green tea catechins (GTCs) have been explored for their anti‐inflammatory and chemopreventive properties, but data on the impact of GTCs on ultraviolet radiation (UVR)‐induced changes to the dermal ECM are lacking.AimTo investigate the effect of an inflammatory dose of solar‐simulated UVR on human dermal ECM and potential for protection by GTCs in a double‐blind randomized controlled trial.MethodsIn total, 50 healthy white (Fitzpatrick skin type I–II) adults aged 18–65 years were randomized to a combination of GTCs 540 mg plus vitamin C 50 mg or to placebo twice daily for 12 weeks. The impact of solar‐simulated UVR at 3 × minimal erythema dose on the dermal collagen and elastic fibre networks was assessed by histology and immunohistochemistry in all participants at baseline. The impact of GTC supplementation on UVR‐induced effects was compared between the groups post‐supplementation.ResultsThe area of papillary dermis covered by collagen and elastic fibres was significantly lower (P < 0.001) in UVR‐exposed skin than in unexposed skin. Significantly lower levels of fibrillin‐rich microfibrils (P = 0.02), fibulin‐2 (P < 0.001) and fibulin‐5 (P < 0.001) were seen in UVR‐exposed than unexposed skin, while procollagen‐1 deposition was significantly higher in UVR‐exposed skin (P = 0.01). Following GTC supplementation, the UVR‐induced change in fibulin‐5 was abrogated in the active group but not the placebo group, with no difference between the two groups for other components.ConclusionsAcute UVR induced significant changes in the human dermal collagen and elastic fibre networks, whereas oral GTCs conferred specific UVR protection to fibulin‐5. Future studies could explore the impact of GTCs on the effects of repeated suberythemal UVR exposure of human skin.

Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT

The COVID-19 pandemic has accelerated the study of drugs, most notably ivermectin and more recently Paxlovid (PF-07321332) which is in phase III clinical trials with experimental data showing covalent binding to the viral protease Mpro. Theoretical developments of catalytic site-directed docking support thermodynamically feasible non-covalent binding to Mpro. Here we show that Paxlovid binds non-covalently at regions other than the catalytic sites with energies stronger than reported and at the same binding site as the ivermectin B1a homologue, all through theoretical methodologies, including blind docking. We volumetrically characterize the non-covalent interaction of the ivermectin homologues (avermectins B1a and B1b) and Paxlovid with the mMpro monomer, through molecular dynamics and scaled particle theory (SPT). Using the fluctuation-dissipation theorem (FDT), we estimated the electric dipole moment fluctuations at the surface of each of complex involved in this study, with similar trends to that observed in the interaction volume. Using fluctuations of the intrinsic volume and the number of flexible fragments of proteins using anisotropic and Gaussian elastic networks (ANM+GNM) suggests the complexes with ivermectin are more dynamic and flexible than the unbound monomer. In contrast, the binding of Paxlovid to mMpro shows that the mMpro-PF complex is the least structurally dynamic of all the species measured in this investigation. The results support a differential molecular mechanism of the ivermectin and PF homologues in the mMpro monomer. Finally, the results showed that Paxlovid despite beingbound in different sites through covalent or non-covalent forms behaves similarly in terms of its structural flexibility and volumetric behaviour.

Wave Delocalization from Clustering in Two-Dimensional Non-Hermitian Disordered Lattices

Localization appears in a variety of phenomena in disordered systems, including a complete halt of electron transport, light localization in photonic structures, and sound localization in elastic networks. This universality stems from a fundamental wave-mechanical phenomenon: the interference between multiple scattering paths. Therefore, the common trend of localization, which is further enhanced by increasing the degree of disorder, is maintained in most wave systems. Here, the presence of delocalization behaviors induced by non-Hermitian disorder is demonstrated in two-dimensional systems, as opposed to the typical disorder-induced localization. A random checkerboard structure that consists of photonic amplifying and dissipating elements is examined as an order-to-disorder generalization of a parity-time-symmetric system. In this non-Hermitian disordered system, we show that the relationship between localization and disorder dramatically changes upon the control of contrast between material phases, even exhibiting a disorder-induced delocalization in the low-contrast regime. This counterintuitive phenomenon originates from the disorder-induced clustering of non-Hermitian material phases, which leads to the unbroken condition of parity-time symmetry. This finding will provide new insight into the multifaceted role of disorder extended by non-Hermitian physics.

Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling

Data-driven building energy load prediction models should be updated dynamically to adapt to building performance degradation and changes of outdoor environment. Conventional dynamic modeling methods usually have to either increase the costs of model training and data storage for obtaining high accuracy, or decrease accuracy for reducing the costs of model training and data storage. Therefore, this paper presented an elastic weight consolidation-based sliding window fine-tuning method for dynamic building energy load prediction modeling with high accuracy, low computational costs, and low data storage costs. It adopted elastic weight consolidation to make a model be able to remember historical knowledge and learn new knowledge without storing historical data. Three common dynamic modeling methods (sliding window retraining, accumulative retraining, and sliding window fine-tuning) and a static modeling method were adopted for performance comparisons with the proposed method using the five-year operational data of a public building. According to the results, the mean absolute error of the proposed method decreased by 66.58%, 9.06%, and 8.70% on average compared with sliding window retraining, sliding window fine-tuning, and static modeling, respectively. It was further demonstrated that the proposed method had better stability than sliding window fine-tuning. Moreover, the proposed method showed similar accuracy to accumulative retraining, while it had lower costs of model training and data storage than accumulative retraining.

Metamaterials you can talk to: Speech recognition with elastic neural networks

To detect spoken commands, smart devices (for example, a speaker with Alexa or Siri) continuously convert acoustic waves to electronic signals, translate them into the digital domain, and analyze them in a signal processor. Each of these steps constantly consumes energy, imposing the need for tethered operation or large batteries. We propose to solve this problem using elastic neural networks, metamaterials consisting of arrays of coupled (potentially nonlinear) resonators. The frequencies and couplings of the resonators are optimized to maximise the speech classification accuracy (energy transmitted when excited with one word but not another). Even in purely linear metastructures, we observe binary classification accuracies exceeding 90% for a large number of pairs of words. This is demonstrated on a dataset from a large and diverse group of speakers. To attain these results, we have developed refined modelling techniques involving localised oscillations and machine learning. A unique feature of metamaterial-based speech processing is that speech classification is entirely passive, requiring no external energy. This is possible due to the very low energy dissipation of elastic waves.

Ionic Conductivity Enhancement in UHMW PEO Gel Electrolytes Based on Room-Temperature Ionic Liquids and Deep Eutectic Solvents.

Physical gels made of poly(ethylene oxide) (PEO) and deep eutectic solvents urea-Li bis(trifluoromethanesulfonyl)imide (TFSI) and ethylene glycol/LiTFSI, or pyrrolidinium ionic liquid solutions PYR13TFSI-LiTFSI and PYR14TFSI-LiTFSI, are prepared by a fast, single-step process, which involves no auxiliary solvents or intermediates and is reproducible and scalable. The properties of these gels are studied as a function of the PEO content and its molecular weight and the nature of the liquid electrolyte. The gels prepared with a low concentration (1–5 wt %) of ultrahigh molecular weight (UHMW) PEO are tough, stretchable materials which resemble soft elastomers and are also self-healing and transparent. Their rheology shows the conventional behavior of physical polymer gels, so that the higher the molecular weight of PEO, the lower the polymer concentration needed to produce the gel. However, the ion conductivities and diffusivities of the gels are striking, in many cases being equal to or significantly higher than those of pure liquid electrolytes. This ion conductivity enhancement is the highest for the lowest PEO concentration with the highest molecular weight. This unprecedented molecular weight dependence of conductivity and diffusivity is the result of two combined effects: the liquid electrolyte chemical structure modification as a consequence of the addition of PEO and the development of elastic networks, where ion mobility and rheology are uncoupled when the PEO added is of UHMW.

Integrating Dually Encapsulated Si Architecture and Dense Structural Engineering for Ultrahigh Volumetric and Areal Capacity of Lithium Storage.

High-theoretical-capacity silicon anodes hold promise in lithium-ion batteries (LIBs). Nevertheless, their huge volume expansion (∼300%) and poor conductivity show the need for the simultaneous introduction of low-density conductive carbon and nanosized Si to conquer the above issues, yet they result in low volumetric performance. Herein, we develop an integration strategy of a dually encapsulated Si structure and dense structural engineering to fabricate a three-dimensional (3D) highly dense Ti3C2Tx MXene and graphene dual-encapsulated Si monolith architecture (HD-Si@Ti3C2Tx@G). Because of its high density (1.6 g cm-3), high conductivity (151 S m-1), and 3D dense dual-encapsulated Si architecture, the resultant HD-Si@Ti3C2Tx@G monolith anode displays an ultrahigh volumetric capacity of 5206 mAh cm-3 (gravimetric capacity: 2892 mAh g-1) at 0.1 A g-1 and a superior long lifespan of 800 cycles at 1.0 A g-1. Notably, the thick and dense monolithic anode presents a large areal capacity of 17.9 mAh cm-2. In-situ TEM and ex-situ SEM techniques, and systematic kinetics and structural stability analysis during cycling demonstrate that such superior volumetric and areal performances stem from its dual-encapsulated Si architecture by the 3D conductive and elastic networks of MXene and graphene, which can provide fast electron and ion transfer, effective volume buffer, and good electrolyte permeability even with a thick electrode, whereas the dense structure results in a large volumetric performance. This work offers a simple and feasible strategy to greatly improve the volumetric and areal capacity of alloy-based anodes for large-scale applications via integrating a dual-encapsulated strategy and dense-structure engineering.

Liquid demixing in elastic networks: Cavitation, permeation, or size selection?

In cells, phase-separated liquid condensates interact mechanically with surrounding elastic networks such as chromatin and cytoskeleton. By considering the trade-offs between elastic, wetting, and interfacial energies, we theoretically show that three droplet phases can be thermodynamically stable: macroscopic droplets that either cavitate or permeate the network, and mesh-size–limited microdroplets. We show that network strain stiffening further enhances this latter size-limitation effect. Our theory predicts the possibility of yet-unobserved droplet phases in the cytoplasm and nucleoplasm.

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both.

Inherited connective tissue diseases highlight macromolecular network interdependences in skin extracellular matrix: a histomorphometric study

ABSTRACT Mutation of just a single extracellular matrix protein, a receptor or enzyme involved in connective tissue metabolism is sufficient to cause systemic pathologies and failure of tissues that are subjected to strong mechanical stresses. Skin histological and computerized image analyses can provide a good qualitative and quantitative indication of these inherited connective tissue diseases. In this study, skin biopsies from young (10 to 25 years) and middle-aged patients (26 to 50 years) suffering from Ehlers-Danlos syndromes (EDS), Marfan syndrome (MS) or pseudoxanthoma elasticum (PXE) were studied after specific staining of both the collagen and elastic networks. Findings from the histomorphometric analyses conducted on skin sections of the patients with EDS, MS and PXE were compared to skin sections of healthy subjects from the same age groups. Our results show that both the collagen and the elastic networks were affected in all the studied pathological cases, but that the adverse changes to the elastic network in older patients were distinct from the physiological changes observed during aging process for healthy subjects. This degenerative process may be explained by an added phenomenon involving a general connective tissue proteolysis.

RigidPy: Rigidity analysis in Python

rigidPy is a Python package that provides a set of tools necessary for studying rigidity and mechanical response in elastic networks. It also includes suitable modules for generating new realizations of networks with applications in glassy systems and protein structures. rigidPy is available freely on GitHub and can be installed using Python Package Index (PyPi). The detailed setup information is provided in this paper, along with an overview of the mathematical framework that has been used in developing the package. Program summaryProgram Title:rigidPyCPC Library link to program files:https://doi.org/10.17632/x3ddt4gbkc.1Developer's repository link:github.com/vardahagh/rigidpyLicensing provisions: MIT licenseProgramming language: Python >3.5Nature of problem: Elasticity, Linear Response, and Structural OptimizationSolution method: Linear Algebra, Convex Optimization

Research Spotlights

Research Spotlights

Flow and Elastic Networks on the 𝑛-Torus: Geometry, Analysis, and Computation

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 31 January 2019Accepted: 30 March 2021Published online: 03 February 2022Keywordsnetwork systems, graph theory, $n$-torus, cycle structure of a graphAMS Subject Headings37C25, 93C10, 37C75, 37C65, 37N35Publication DataISSN (print): 0036-1445ISSN (online): 1095-7200Publisher: Society for Industrial and Applied MathematicsCODEN: siread

A mechanistic view on the aging human skin through ex vivo layer-by-layer analysis of mechanics and microstructure of facial and mammary dermis

Age-related changes in skin mechanics have a major impact on the aesthetic perception of skin. The link between skin microstructure and mechanics is crucial for therapeutic and cosmetic applications as it bridges the micro- and the macro-scale. While our perception is governed by visual and tactile changes at the macroscopic scale, it is the microscopic scale (molecular assemblies, cells) that is targeted by topical treatments including active compounds and energies. We report here a large dataset on freshly excised human skin, and in particular facial skin highly relevant for cosmetics and aesthetic procedures. Detailed layer-by-layer mechanical analysis revealed significant age-dependent decrease in stiffness and elastic recoil of full-thickness skin from two different anatomical areas. In mammary skin, we found that the onset of mechanical degradation was earlier in the superficial papillary layer than in the deeper, reticular dermis. These mechanical data are linked with microstructural alterations observed in the collagen and elastic networks using staining and advanced imaging approaches. Our data suggest that with ageing, the earliest microstructural and mechanical changes occur in the top-most layers of dermis/skin and then propagate deeper, providing an opportunity for preventive topical treatments acting at the level of papillary dermis.

Mechanical response in elastic fluid flow networks

Compliance in fluid carrying vessels is the capability of interacting with flow and attenuating waves via mechanisms such as the Windkessel effect. In this paper we investigate how vessel compliance impacts the dynamics of flow and pressure in the transient regime shortly after changes in boundary conditions. We show that individual vessels as well as networks composed of many such vessels can exist in one of two possible regimes, one in which transient dynamics are fast and one in which it is slow. These results can be used to analyze the response capabilities of biological flow networks such as the animal vasculature.

Responsive Telechelic Block Copolymers for Enhancing the Elasticity of Nanoemulsions

The rheology of emulsions dictates their performance in many scientific and industrial applications. Here, we demonstrate that high-molecular-weight telechelic triblock copolymers composed of polystyrene end blocks with a poly(ethylene oxide) midblock effectively modify the rheology of a suspension of cyclohexane nanodroplets. Because of their telechelic structure, these polymers bridge between droplets to generate elastic networks with shear moduli on the order of 1000 Pa. The network elasticity increases with polymer concentration and molecular weight as the polymers more effectively form bridges. Furthermore, we tune the nanoscale interactions that control the polymer partitioning by decreasing the temperature below the cloud point of the system. At low temperatures, the polystyrene end blocks no longer preferentially partition into the cyclohexane droplets, and the emulsions begin to exhibit terminal viscous relaxations. These results identify key properties of telechelic block copolymers that can be exploited to significantly enhance nanoemulsion elasticity to improve the processability and transport of complex fluids in applications ranging from 3D printing to functional hydrogels.

ON DYNAMICS OF ELASTIC NETWORKS WITH RIGID JUNCTIONS WITHIN NONLINEAR MICRO-POLAR ELASTICITY

ON DYNAMICS OF ELASTIC NETWORKS WITH RIGID JUNCTIONS WITHIN NONLINEAR MICRO-POLAR ELASTICITY