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We use coarse-grained molecular dynamics simulations to study deformation of networks and gels of linear and brush strands in both linear and nonlinear deformation regimes under constant pressure conditions. The simulations show that the Poisson ratio of networks and gels could exceed 0.5 in the nonlinear deformation regime. This behavior is due to the ability of the network and gel strands to sustain large reversible deformation, which, in combination with the finite strand extensibility results in strand alignment and monomer density, increases with increasing strand elongation. We developed a nonlinear network and gel deformation model which defines conditions for the Poisson ratio to exceed 0.5. The model predictions are in good agreement with the simulation results.
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Resorbable, implantable bioelectronic devices are emerging as powerful tools to reliably monitor critical physiological parameters in real time over extended periods. While degradable magnesium-based electronics have pioneered this effort, relatively short functional lifetimes have slowed clinical translation. Barrier films that are both flexible and resorbable over predictable timelines would enable tunability in device lifetime and expand the viability of these devices. Herein, we present a library of stereocontrolled succinate-based copolyesters which leverage copolymer composition and processing method to afford tunability over thermomechanical, crystalline, and barrier properties. One copolymer composition within this library has extended the functional lifetime of transient bioelectronic prototypes over existing systems by several weeks-representing a considerable step towards translational devices.
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Eletrônica , Polímeros , Polímeros/químicaRESUMO
The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, nx, side chains, nsc, backbone spacers between side chains, ng. Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics S = [l, v, b] defined by monomer projection length l and excluded volume v, Kuhn length b of bare backbone and side chains, and architecture A = [nsc, ng, nx] of polymer networks and their equilibrium mechanical properties P = [G, ß] including the structural shear modulus G and firmness parameter ß. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly(n-butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus G < 50 kPa and 0.01 ≤ ß ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands.
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Our lives cannot be imagined without polymer networks, which range widely, from synthetic rubber to biological tissues. Their properties-elasticity, strain-stiffening and stretchability-are controlled by a convolution of chemical composition, strand conformation and network topology. Yet, since the discovery of rubber vulcanization by Charles Goodyear in 1839, the internal organization of networks has remained a sealed 'black box'. While many studies show how network properties respond to topology variation, no method currently exists that would allow the decoding of the network structure from its properties. We address this problem by analysing networks' nonlinear responses to deformation to quantify their crosslink density, strand flexibility and fraction of stress-supporting strands. The decoded structural information enables the quality control of network synthesis, comparison of targeted to actual architecture and network classification according to the effectiveness of stress distribution. The developed forensic approach is a vital step in future implementation of artificial intelligence principles for soft matter design.
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Hot-melt pressure-sensitive adhesives (HMPSAs) are used in applications from office supplies to biomedical adhesives. The major component in HMPSA formulations is thermoplastic elastomers, such as styrene-based block copolymers, that provide both mechanical integrity and moldability. Since neat polymer networks are unable to establish an adhesive bond, large quantities of plasticizers and tackifiers are added. These additives enhance the adhesive performance but complicate the phase behavior and property stability of the pressure-sensitive adhesive. Herein, we introduce an alternative additive-free approach to HMPSA design based on self-assembly of bottlebrush graft-copolymers, where side chains behave as softness, strength, and viscoelasticity mediators. These systems maintain moldability of conventional thermoplastic elastomers, while architecturally disentangled bottlebrush network strands empower several benefits such as extreme softness for substrate wetting, low melt viscosity for molding and 3D-printing, and a broad frequency range of viscoelastic responses for adhesion regulation within almost four orders of magnitude. The brush graft-copolymers implement five independently controlled architectural parameters to regulate the Rouse time, work of adhesion, and debonding mechanisms.
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Most elastomers undergo strain-induced crystallization (SIC) under tension; as individual chains are held rigidly in a fixed position by an applied strain, their alignment along the strain field results in a shift from strain-hardening (SH) to SIC. A similar degree of stretching is associated with the tension necessary to accelerate mechanically coupled, covalent chemical responses of mechanophores in overstretched chains, raising the possibility of an interplay between the macroscopic response of SIC and the molecular response of mechanophore activation. Here, thiol-yne-derived stereoelastomers doped covalently with a dipropiolate-derivatized spiropyran (SP) mechanophore (0.25-0.38 mol%) are reported. The material properties of SP-containing films are consistent with undoped controls, indicating that the SP is a reporter of the mechanical state of the polymer. Uniaxial tensile tests reveal correlations between mechanochromism and SIC, which are strain-rate-dependent. When mechanochromic films are stretched slowly to the point of mechanophore activation, the covalently tethered mechanophore remains trapped in a force-activated state, even after the applied stress is removed. Mechanophore reversion kinetics correlate with the applied strain rate, resulting in highly tunable decoloration rates. Because these polymers are not covalently crosslinked, they are recyclable by melt-pressing into new films, increasing their potential range of strain-sensing, morphology-sensing, and shape-memory applications.
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Since the pioneering research by Staudinger on dilute solution viscosity and its relation to the polymer molecular weight, viscosity analysis has become a valuable technique for polymer characterization. The conventional approach is based on the Huggins approximation of the solution-specific viscosity by a quadratic function of concentration, c. We show how to reformulate this approach in a universal form by representing a solution-specific viscosity, ηsp, as a generalized universal function ηsp(c) = α(c/c*) + (1 - α)(c/c*)2 of chain overlap concentration, c*, determined at ηsp = 1, with numerical coefficients α = 0.745 ± 0.005 for good and 0.625 ± 0.008 for a θ solvent. This viscosity representation can be viewed as a calibration curve for molecular weight determination from a measurement of the solution viscosity at a given solution concentration. Furthermore, the molecular weight dependence of the overlap concentration provides a means for quantifying the polymer/solvent affinity and the solvent effect on chain flexibility. The extension of the approach to semidilute solutions opens a path for obtaining molecular weight in a broad concentration range without requiring a dilution and monitoring its change during the polymerization reaction from solution viscosity.
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Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters-side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use.
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Polymeric networks are commonly used for various biomedical applications, from reconstructive surgery to wearable electronics. Some materials may be soft, firm, strong, or damping however, implementing all four properties into a single material to replicate the mechanical properties of tissue has been inaccessible. Herein, we present the A-g-B brush-like graft copolymer platform as a framework for fabrication of materials with independently tunable softness and firmness, capable of reaching a strength of â¼10 MPa on par with stress-supporting tissues such as blood vessel, muscle, and skin. These properties are maintained by architectural control, therefore diverse mechanical phenotypes are attainable for a variety of different chemistries. Utilizing this attribute, we demonstrate the capability of the A-g-B platform to enhance specific characteristics such as tackiness, damping, and moldability.
Assuntos
Elastômeros , Polímeros , EletrônicaRESUMO
Mechanically diverse polymer gels are commonly integrated into biomedical devices, soft robots, and tissue engineering scaffolds to perform distinct yet coordinated functions in wet environments. Such multigel systems are prone to volume fluctuations and shape distortions due to differential swelling driven by osmotic solvent redistribution. Living systems evade these issues by varying proximal tissue stiffness at nearly equal water concentration. However, this feature is challenging to replicate with synthetic gels: any alteration of cross-link density affects both the gel's swellability and mechanical properties. In contrast to the conventional coupling of physical properties, we report a strategy to tune the gel modulus independent of swelling ratio by regulating network strand flexibility with brushlike polymers. Chemically identical gels were constructed with a broad elastic modulus range at a constant solvent fraction by utilizing multidimensional network architectures. The general design-by-architecture framework is universally applicable to both organogels and hydrogels and can be further adapted to different practical applications.
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The remarkable elasticity and tensile strength found in natural elastomers are challenging to mimic. Synthetic elastomers typically feature covalently cross-linked networks (rubbers), but this hinders their reprocessability. Physical cross-linking via hydrogen bonding or ordered crystallite domains can afford reprocessable elastomers, but often at the cost of performance. Herein, we report the synthesis of ultra-tough, reprocessable elastomers based on linear alternating polymers. The incorporation of a rigid isohexide adjacent to urethane moieties affords elastomers with exceptional strain hardening, strain rate dependent behavior, and high optical clarity. Distinct differences were observed between isomannide and isosorbide-based elastomers where the latter displays superior tensile strength and strain recovery. These phenomena are attributed to the regiochemical irregularities in the polymers arising from their distinct stereochemistry and respective inter-chain hydrogen bonding.
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Elastômeros , Isossorbida , Elastômeros/química , Ligação de Hidrogênio , Isossorbida/químicaRESUMO
Stereochemistry in polymers can be used as an effective tool to control the mechanical and physical properties of the resulting materials. Typically, though, in synthetic polymers, differences among polymer stereoisomers leads to incremental property variation, i.e., no changes to the baseline plastic or elastic behavior. Here we show that stereochemical differences in sugar-based monomers yield a family of nonsegmented, alternating polyurethanes that can be either strong amorphous thermoplastic elastomers with properties that exceed most cross-linked rubbers or robust, semicrystalline thermoplastics with properties comparable to commercial plastics. The stereochemical differences in the monomers direct distinct intra- and interchain supramolecular hydrogen-bonding interactions in the bulk materials to define their behavior. The chemical similarity among these isohexide-based polymers enables both statistical copolymerization and blending, which each afford independent control over degradability and mechanical properties. The modular molecular design of the polymers provides an opportunity to create a family of materials with divergent properties that possess inherently built degradability and outstanding mechanical performance.
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We apply a scaling theory of semidilute polymer solutions to quantify solution properties of polysaccharides such as galactomannan, chitosan, sodium carboxymethyl cellulose, hydroxypropyl methyl cellulose, methyl cellulose, xanthan, apple pectin, cellulose tris(phenyl carbamate), hydroxyethyl cellulose, hydroxypropyl cellulose, sodium hyaluronate, sodium alginate, and sodium κ-carrageenan. In particular, we obtain the molar mass of the chain segment inside a correlation blob M g = BÌ 3/(3ν-1) c 1/(1-3ν) as a function of concentration c, interaction parameter BÌ, and exponent ν. Parameter BÌ assumes values BÌ g, BÌ th and M 0/N A 1/3 l for exponents v = 0.588, 0.5 and 1, respectively, where M 0 is the molar mass of a repeat unit, l is the projection length of a repeat unit, and N A is the Avogadro number. In the different solution regimes, the values of the BÌ-parameters are extracted from the plateaus of the normalized specific viscosity ηsp (c)/M w c 1/(3ν-1), where M w is the weight-average molecular weight of the polymer chain. The values of the BÌ-parameters are used in calculations of the excluded volume v, Kuhn length b, and crossover concentrations c*, c th, and c** into a semidilute polymer solution, a solution of overlapping thermal blobs and a concentrated polymer solution, respectively. This information is summarized as a diagram of states of different polysaccharide solution regimes by implementing a v/bl 2 and c/c** representation. The scaling approach is extended to the entangled solution regime, allowing us to obtain the chain packing number, PÌ e. This completes the set of parameters {BÌ g, BÌ th, PÌ e} which uniquely describes the static and dynamic properties of a polysaccharide solution.
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Softness and firmness are seemingly incompatible traits that synergize to create the unique soft-yet-firm tactility of living tissues pursued in soft robotics, wearable electronics, and plastic surgery. This dichotomy is particularly pronounced in tissues such as fat that are known to be both ultrasoft and ultrafirm. However, synthetically replicating this mechanical response remains elusive since ubiquitously employed soft gels are unable to concurrently reproduce tissue firmness. We have addressed the tissue challenge through the self-assembly of linear-bottlebrush-linear (LBL) block copolymers into thermoplastic elastomers. This hybrid molecular architecture delivers a hierarchical network organization with a cascade of deformation mechanisms responsible for initially low moduli followed by intense strain-stiffening. By bridging the firmness gap between gels and tissues, we have replicated the mechanics of fat, fetal membrane, spinal cord, and brain tissues. These solvent-free, nonleachable, and tissue-mimetic elastomers also show enhanced biocompatibility as demonstrated by cell proliferation studies, all of which are vital for the safety and longevity of future biomedical devices.
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Planar, terpyridine-based metal complexes with the Sierpinski triangular motif and alkylated corners undergo a second self-assembly event to give megastructural Sierpinski pyramids; assembly is driven by the facile lipophilic-lipophilic association of the alkyl moieties and complementary perfect fit of the triangular building blocks. Confirmation of the 3D, pyramidal structures was verified and supported by a combination of TEM, AFM, and multiscale simulation techniques.
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The motion of nanoparticles on soft surfaces is the result of interplay between capillary, elastic and friction forces. To elucidate the importance of the different contributions controlling nanoparticle rolling dynamics on soft surfaces, we performed molecular dynamics simulations of solid nanoparticles in contact with soft elastic substrates. The nanoparticle motion is initiated by applying a constant force resulting in stationary, steady rolling, and accelerating states, depending on the nanoparticle-substrate work of adhesion, W, the magnitude of the net applied force, F, and the substrate shear modulus G. In the stationary state, the restoring torque produced in the contact area balances the torque due to the external force. The rolling force Fr, determining the crossover to the rolling state, is proportional to the product of the work of adhesion W and nanoparticle size Rp, Fr â¼ WRp. In the steady rolling state, F > Fr, the nanoparticle maintains a constant rolling velocity which is a manifestation of the balance between the rolling friction force and the applied force. The observed scaling relationships between the applied force and nanoparticle velocity reflect a viscoelastic nature of the substrate deformation dynamics. A nanoparticle begins to accelerate when the energy supplied to the nanoparticle exceeds the energy dissipated in the contact area due to viscoelastic substrate deformation. Using these simulation results, we have constructed a diagram of states in terms of the dimensionless parameters F/WRp and W/GRp.
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Nanoparticles have been recently shown to be able to act as effective adhesives capable of binding two soft materials together. We performed coarse-grained molecular dynamics simulations to study contact mechanics of soft nanoparticles at the interfaces between two elastic surfaces. Depending on the nanoparticle size as well as the substrates' elastic and interfacial properties, a nanoparticle at the interface between two elastic substrates could be in a bridging or Pickering state. The degree of penetration of a nanoparticle into a substrate is shown to be determined by nanoparticle size, strength of nanoparticle-substrate interactions, and nanoparticle and substrate elastic properties. Using the weighted histogram analysis method, we calculated the potential of mean force for separation of two substrates whose interface was reinforced by deformable nanoparticles. These simulations show that interface reinforcement is a function of nanoparticle size and elastic modulus. The most effective reinforcement of the interface was observed for the softest nanoparticles which could result in close to 8 times increase in the work of adhesion.
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ABA triblock copolymers composed of a poly(dimethylsiloxane) (PDMS) bottlebrush central block and linear poly(methyl methacrylate) (PMMA) terminal blocks self-assemble into a physical network of PDMS bottlebrush strands connected by PMMA spherical domains. A combination of small- and ultrasmall-angle X-ray scattering techniques was used to concurrently examine dimensions of PMMA spherical domains and PDMS bottlebrush strands both in the bulk and at the PMMA-PDMS interface. In agreement with scaling model predictions, the degrees of polymerization of the bottlebrush backbone (nbb) and PMMA block (nA) correlate with the measured PMMA domain size and area per molecule at the PMMA-PDMS interface as DA â (nbbnA)1/3 and S â nA2/3nbb-1/3, respectively. In the bulk, bottlebrush strands are extended due to steric repulsion between the side chains and unfavorable interactions between the different blocks. At the PMMA-PDMS interface with large curvature, packing constraints require additional bottlebrush backbone extension and alignment of side chains along the backbone in the direction perpendicular to the interface.
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Dynamics of melts and solutions of high molecular weight polymers and biopolymers is controlled by topological constraints (entanglements) imposing a sliding chain motion along an effective confining tube. For linear chains, the tube size is determined by universal packing number Pe, the number of polymer strands within a confining tube that is required for chains to entangle. Here we show that in melts of brush-like (graft) polymers, consisting of linear chain backbones with grafted side chains, Pe is not a universal number and depends on the molecular architecture. In particular, we use coarse-grained molecular dynamics simulations to demonstrate that the packing number is a nonmonotonic function of the ratio Rnsc/Rng of the size of the side chains Rnsc to that of the backbone spacer between neighboring grafting points Rng. This parameter characterizes the degree of mutual interpenetration between side chains of the same macromolecule. We show that Pe of brush-like polymers first decreases with increasing side chain grafting density in the dilute side chain regime (Rnsc < Rng), then begins to increase in the regime of overlapping side chains (Rnsc > Rng), approaching the value for linear chains in the limit of densely grafted side chains. This dependence of the packing number reflects a crossover from chain-like entanglements in systems with loosely grafted side chains (comb-like polymers) to entanglements between flexible filaments (bottlebrush-like polymers). Our simulation results are in agreement with the experimental data for the dependence of a plateau modulus on the molecular architecture of graft poly(n-butyl acrylates) and poly(norbornene)-graft-poly(lactide) melts.