Dataset Information

Topoarchitected polymer networks expand the space of material properties.

ABSTRACT: Many living tissues achieve functions through architected constituents with strong adhesion. An Achilles tendon, for example, transmits force, elastically and repeatedly, from a muscle to a bone through staggered alignment of stiff collagen fibrils in a soft proteoglycan matrix. The collagen fibrils align orderly and adhere to the proteoglycan strongly. However, synthesizing architected materials with strong adhesion has been challenging. Here we fabricate architected polymer networks by sequential polymerization and photolithography, and attain adherent interface by topological entanglement. We fabricate tendon-inspired hydrogels by embedding hard blocks in topological entanglement with a soft matrix. The staggered architecture and strong adhesion enable high elastic limit strain and high toughness simultaneously. This combination of attributes is commonly desired in applications, but rarely achieved in synthetic materials. We further demonstrate architected polymer networks of various geometric patterns and material combinations to show the potential for expanding the space of material properties.

SUBMITTER: Liu X

PROVIDER: S-EPMC8956700 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Topoarchitected polymer networks expand the space of material properties.

Liu Xiao X Wu Jingping J Qiao Keke K Liu Guohan G Wang Zhengjin Z Lu Tongqing T Suo Zhigang Z Hu Jian J

Nature communications 20220325 1

Many living tissues achieve functions through architected constituents with strong adhesion. An Achilles tendon, for example, transmits force, elastically and repeatedly, from a muscle to a bone through staggered alignment of stiff collagen fibrils in a soft proteoglycan matrix. The collagen fibrils align orderly and adhere to the proteoglycan strongly. However, synthesizing architected materials with strong adhesion has been challenging. Here we fabricate architected polymer networks by sequent ...[more]

PMID: 35338139

Similar Datasets

Project description:The formed morphology during phase separation is crucial for determining the properties of the resulting product, e.g., a functional membrane. However, an accurate morphology prediction is challenging due to the inherent complexity of molecular interactions. In this study, the phase separation of a two-dimensional model polymer solution is investigated. The spinodal decomposition during the formation of polymer-rich domains is described by the Cahn-Hilliard equation incorporating the Flory-Huggins free energy description between the polymer and solvent. We circumvent the heavy burden of precise morphology prediction through two aspects. First, we systematically analyze the degree of impact of the parameters (initial polymer volume fraction, polymer mobility, degree of polymerization, surface tension parameter, and Flory-Huggins interaction parameter) in a phase-separating system on morphological evolution characterized by geometrical fingerprints to determine the most influential factor. The sensitivity analysis provides an estimate for the error tolerance of each parameter in determining the transition time, the spinodal decomposition length, and the domain growth rate. Secondly, we devise a set of physics-informed neural networks (PINN) comprising two coupled feedforward neural networks to represent the phase-field equations and inversely discover the value of the embedded parameter for a given morphological evolution. Among the five parameters considered, the polymer-solvent affinity is key in determining the phase transition time and the growth law of the polymer-rich domains. We demonstrate that the unknown parameter can be accurately determined by renormalizing the PINN-predicted parameter by the change of characteristic domain size in time. Our results suggest that certain degrees of error are tolerable and do not significantly affect the morphology properties during the domain growth. Moreover, reliable inverse prediction of the unknown parameter can be pursued by merely two separate snapshots during morphological evolution. The latter largely reduces the computational load in the standard data-driven predictive methods, and the approach may prove beneficial to the inverse design for specific needs.

Project description:In this work, we extend an approach to coarse-grained (CG) modeling for polymer melts in which the conservative potential is parametrized using the iterative Boltzmann inversion (IBI) method and the accelerated dynamics inherent to IBI are corrected using the dissipative Langevin thermostat with a single tunable friction parameter (J. Chem. Phys. 2021, 154, 084114). Diffusive measures from picoseconds to nanoseconds are used to determine the Langevin friction factor to apply to the CG model to recover all-atom (AA) dynamics; the resulting friction factors are then compared for consistency. Here, we additionally parametrize the CG dynamics using a material property, the zero-shear viscosity, which we measure using the Green-Kubo (GK) method. Two materials are studied, squalane as a function of temperature and the same polystyrene oligomers previously studied as a function of chain length. For squalane, the friction derived from the long-time diffusive measures and the viscosity all strongly increase with decreasing temperature, showing an Arrhenius-like dependence, and remain consistent with each other over the entire temperature range. In contrast, the friction required for the picosecond diffusive measurement, the Debye-Waller factor, is somewhat lower than the friction from long-time measures and relatively insensitive to temperature. A time-dependent friction would be required to exactly reproduce the AA measurements during the caging transition connecting these two extremes over the entire timespan at this level of coarse-graining. For the polystyrene oligomers for which we previously characterized the diffusive friction, the viscosity-parametrized frictions are consistent with the diffusive measures for the smallest chain length. However, for the longer chains, we find different trends based on measurement method with friction derived from rotational diffusion remaining nearly constant, friction derived from translational diffusion showing a modestly increasing trend, and viscosity-derived friction showing a modest decreasing trend. This seems to indicate that there is some sensitivity of the friction measurement method for systems with increased relaxation times and that in particular, the unsteady dynamics of the individual parametrization schemes plays a role in this. Increased difficulty in applying the GK method with increasing relaxation time of the longer chain systems is also discussed. Overall, we find that when the material is in a high-temperature melt state and the viscosity measurement is reliable, the friction parametrization from the diffusive friction measures is consistent and the lower cost diffusive parametrization is a reliable means for modeling viscosity. Our data give insight into the time-dependent friction one might compute using a non-Markovian approach to enable the recovery of AA dynamics over a wider range of time scales than can be computed using a single friction.

Dataset Information

Topoarchitected polymer networks expand the space of material properties.

Publications

Topoarchitected polymer networks expand the space of material properties.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets