Mimicking biological stress-strain behaviour with synthetic elastomers.

Despite the versatility of synthetic chemistry, certain combinations of mechanical softness, strength, and toughness can be difficult to achieve in a single material. These combinations are, however, commonplace in biological tissues, and are therefore needed for applications such as medical implants, tissue engineering, soft robotics, and wearable electronics. Present materials synthesis strategies are predominantly Edisonian, involving the empirical mixing of assorted monomers, crosslinking schemes, and occluded swelling agents, but this approach yields limited property control. Here we present a general strategy for mimicking the mechanical behaviour of biological materials by precisely encoding their stress-strain curves in solvent-free brush- and comb-like polymer networks (elastomers). The code consists of three independent architectural parameters-network strand length, side-chain length and grafting density. Using prototypical poly(dimethylsiloxane) elastomers, we illustrate how this parametric triplet enables the replication of the strain-stiffening characteristics of jellyfish, lung, and arterial tissues.

Coarse-Grained Artificial Intelligence for Design of Brush Networks.

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.

Bottlebrush Thermoplastic Elastomers as Hot-Melt Pressure-Sensitive Adhesives.

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.

Study of the synergistic influence of zwitterionic interactions and graphene oxide on water diffusion mechanism and mechanical properties in hybrid hydrogel network.

Hybrid hydrogels based on n-isopropylacrylamide, zwitterionic comonomer, and graphene oxide were synthesized to study their physical and mechanical properties. The compositional variation largely influenced the swelling characteristics of the hybrid hydrogels compared to mechanical properties, i.e., elongation and compression. Additionally, Rheometric swelling measurements on the swollen hydrogels were performed until they reached equilibrium showed a very low phase angle δ indicating strong covalent network, which intrun increases with increasing content of zwitterions and GO. Swelling kinetics were studied and found to follow Fickian dynamics, albeit zwitterion-containing gels showed a peculiar 2-step swelling pattern. Interestingly, differences in the swelling mechanism are also clear for the hydrogels with 2D GO (Graphene oxide) nano-fillers from its 1D nano-filler CNTs (Carbon nanotubes). In elongation, the samples break in a brittle fashion at Hencky strains εmax around 0.4-0.65 with the maximum stress being observed for samples with high Zw-content and 0.2% GO, which can be explained by the stress-rising properties of sharp edges of GO. In contrast, the data in compression profits from higher GO-contents as crack growth is less important in this deformation mode. This work will contribute to future composite gel applications.

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks.

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.

Injectable bottlebrush hydrogels with tissue-mimetic mechanical properties.

Injectable hydrogels are desired in many biomedical applications due to their minimally invasive deployment to the body and their ability to introduce drugs. However, current injectables suffer from mechanical mismatch with tissue, fragility, water expulsion, and high viscosity. To address these issues, we design brush-like macromolecules that concurrently provide softness, firmness, strength, fluidity, and swellability. The synthesized linear-bottlebrush-linear (LBL) copolymers facilitate improved injectability as the compact conformation of bottlebrush blocks results in low solution viscosity, while the thermoresponsive linear blocks permit prompt gelation at 37°C. The resulting hydrogels mimic the deformation response of supersoft tissues such as adipose and brain while withstanding deformations of 700% and precluding water expulsion upon gelation. Given their low cytotoxicity and mild inflammation in vivo, the developed materials will have vital implications for reconstructive surgery, tissue engineering, and drug delivery applications.

Mechanically Diverse Gels with Equal Solvent Content.

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.

Super-soft, firm, and strong elastomers toward replication of tissue viscoelastic response.

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 ∼10 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.

Injectable non-leaching tissue-mimetic bottlebrush elastomers as an advanced platform for reconstructive surgery.

Current materials used in biomedical devices do not match tissue's mechanical properties and leach various chemicals into the body. These deficiencies pose significant health risks that are further exacerbated by invasive implantation procedures. Herein, we leverage the brush-like polymer architecture to design and administer minimally invasive injectable elastomers that cure in vivo into leachable-free implants with mechanical properties matching the surrounding tissue. This strategy allows tuning curing time from minutes to hours, which empowers a broad range of biomedical applications from rapid wound sealing to time-intensive reconstructive surgery. These injectable elastomers support in vitro cell proliferation, while also demonstrating in vivo implant integrity with a mild inflammatory response and minimal fibrotic encapsulation.

Bottlebrush Bridge between Soft Gels and Firm Tissues.

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.

Strained Bottlebrushes in Super-Soft Physical Networks.

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 ∝ (nbbnA)1/3 and S ∝ 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.

Encoding tissue mechanics in silicone.

By controlling polymer-network architecture, we outline a roadmap for the creation of tissue-like materials to extend the performance of soft robots to harsh environments.

Universal Coatings Based on Zwitterionic-Dopamine Copolymer Microgels.

Multifunctional coatings that adhere to chemically distinct substrates are vital in many industries, including automotive, aerospace, shipbuilding, construction, petrochemical, biomedical, and pharmaceutical. We design well-defined, nearly monodisperse microgels that integrate hydrophobic dopamine methacrylamide monomers and hydrophilic zwitterionic monomers. The dopamine functionalities operate as both intraparticle cross-linkers and interfacial binders, respectively providing mechanical strength of the coatings and their strong adhesion to different substrates. In tandem, the zwitterionic moieties enable surface hydration to empower antifouling and antifogging properties. Drop-casting of microgel suspensions in ambient as well as humid environments facilitates rapid film formation and tunable roughness through regulation of cross-linking density and deposition conditions.

Chameleon-like elastomers with molecularly encoded strain-adaptive stiffening and coloration.

Active camouflage is widely recognized as a soft-tissue feature, and yet the ability to integrate adaptive coloration and tissuelike mechanical properties into synthetic materials remains elusive. We provide a solution to this problem by uniting these functions in moldable elastomers through the self-assembly of linear-bottlebrush-linear triblock copolymers. Microphase separation of the architecturally distinct blocks results in physically cross-linked networks that display vibrant color, extreme softness, and intense strain stiffening on par with that of skin tissue. Each of these functional properties is regulated by the structure of one macromolecule, without the need for chemical cross-linking or additives. These materials remain stable under conditions characteristic of internal bodily environments and under ambient conditions, neither swelling in bodily fluids nor drying when exposed to air.

Programming temporal shapeshifting.

Shapeshifting enables a wide range of engineering and biomedical applications, but until now transformations have required external triggers. This prerequisite limits viability in closed or inert systems and puts forward the challenge of developing materials with intrinsically encoded shape evolution. Herein we demonstrate programmable shape-memory materials that perform a sequence of encoded actuations under constant environment conditions without using an external trigger. We employ dual network hydrogels: in the first network, covalent crosslinks are introduced for elastic energy storage, and in the second one, temporary hydrogen-bonds regulate the energy release rate. Through strain-induced and time-dependent reorganization of the reversible hydrogen-bonds, this dual network allows for encoding both the rate and pathway of shape transformations on timescales from seconds to hours. This generic mechanism for programming trigger-free shapeshifting opens new ways to design autonomous actuators, drug-release systems and active implants.