Brush Architecture and Network Elasticity: Path to the Design of Mechanically Diverse Elastomers

Bottlebrush Networks: A Primer for Advanced Architectures

Bottlebrush networks (BBNs) are an exciting new class of materials with interesting physical properties derived from their unique architecture. While great strides have been made in our fundamental understanding of bottlebrush polymers and networks, an interdisciplinary approach is necessary for the field to accelerate advancements. This review aims to act as a primer to BBN chemistry and physics for both new and current members of the community. In addition to providing an overview of contemporary BBN synthetic methods, we developed a workflow and desktop application (LengthScale), enabling bottlebrush physics to be more approachable. We conclude by addressing several topical issues and asking a series of pointed questions to stimulate conversation within the community.

Inorganic Bottlebrush and Comb Polymers as a Platform for Supersoft, Solvent-Free Elastomers

Due to their unique rheological and mechanical properties, bottlebrush polymers are inimitable components of biological and synthetic systems such as cartilage and ultrasoft elastomers. However, while their rheological properties can be precisely controlled through their macromolecular structures, the current chemical spectrum available is limited to a handful of synthetic polymers with aliphatic carbon backbones. Herein we design and synthesize a series of inorganic bottlebrush polymers based on a unique combination of polydimethylsiloxane (PDMS) and polyphosphazene (PPz) chemistry. This non-carbon-based platform allows for simple variation of the significant architectural dimensions of bottlebrush-polymer-based elastomers. Grafting PDMS to PPz and vice versa also allows us to further exploit the unique properties of these polymers combined in a single material. These novel hybrid bottlebrush polymers were cured to give supersoft, solvent-free elastomers. We systematically studied the effect of architectural parameters and chemical functionality on their rheological properties. Besides forming supersoft elastomers, the energy dissipation characteristics of the elastomers were observed to be considerably higher than those for PDMS-based elastomers. Hence this work introduces a robust synthetic platform for solvent-free supersoft elastomers with potential applications as biomimetic damping materials.

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, β] including the structural shear modulus G and firmness parameter β. 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 ≤ β ≤ 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.

3D printable soft and solvent-free thermoplastic elastomer containing dangling bottlebrush chains

Polymer networks containing bottlebrush chains are emerging materials with exceptionally soft and highly tunable mechanical properties. However, such materials have not been extensively implemented in functional processing techniques such as three-dimensional (3D) printing. Here, we introduce a new design of soft and solvent-free polydimethylsiloxane (PDMS)-based thermoplastic elastomer which contains dangling and space-filling bottlebrush chains, featuring a yield stress and a rapid recovery after stress removal; both required for high spatial fidelity 3D printing. The developed material is composed of two copolymers; the main building block is a diblock copolymer with linear polystyrene (PS) block and bottlebrush PDMS block (PS-b-bbPDMS) while the second component is PS-b-PDMS-b-PS triblock, self-assembling to a physical network. This design provides independent tunability of each structural parameter on the molecular level, hence, macroscopic control of the materials' mechanical properties. Multiple self-supportive 3D structures with spanning elements are 3D printed at elevated temperatures using a developed material with a low shear modulus of G' = 3.3 kPa containing 3 : 1 molar ratio of diblock to triblock copolymers without the need for volatile solvent, or post-treatment. This 3D printing compatible design opens new opportunities to utilize the distinctive mechanical properties of bottlebrush materials for applications such as soft tissue scaffolds, sensors, actuators, and soft robots.

Forensics of polymer networks

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.

Controlling the mechanical properties of hydrogels via modulating the side-chain length

Even though the toughness of hydrogels is usually adjusted by changing the cross-linking density and structure, or the polymer concentration, we have discovered a new strategy to control the toughness via modulating the side-chain length. In this study, this strategy was applied to biocompatible poly(oligo(ethylene glycol) methyl ether methacrylate) with long ethylene-oxide side chains.

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.

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.

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.