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Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines. Here, a human‐in‐the‐loop reinforcement learning (RL) approach is used to discover polyurethane elastomers that overcome pervasive stress–strain property tradeoffs. Starting with a diverse training set of 92 formulations, a coupled multi‐component reward system was identified that guides RL agents toward materials with both high strength and extensibility. Through three rounds of iterative optimization combining RL predictions with human chemical intuition, we identified elastomers with more than double the average toughness compared to the initial training set. The final exploitation round, aided by solubility prescreening, predicted twelve materials exhibiting both high strength (>10 MPa) and high strain at break (>200%). Analysis of the high‐performing materials revealed structure‐property insights, including the benefits of high molar mass urethane oligomers, a high density of urethane functional groups, and incorporation of rigid low molecular weight diols and unsymmetric diisocyanates. These findings demonstrate that machine‐guided, human‐augmented design is a powerful strategy for accelerating polymer discovery in applications where data is scarce and expensive to acquire, with broad applicability to multi‐objective materials optimization. 

Abstract Self‐assembled networks of bottlebrush copolymers are promising materials for biomedical applications due to a unique combination of ultra‐softness and strain‐adaptive stiffening, characteristic of soft biological tissues. Transitioning from ABA linear‐brush‐linear triblock copolymers to A‐g‐B bottlebrush graft copolymer architectures allows significant increasing the mechanical strength of thermoplastic elastomers. Using real‐time synchrotron small‐angle X‐ray scattering, it is shown that annealing of A‐g‐B elastomers in a selective solvent for the linear A blocks allows for substantial network reconfiguration, resulting in an increase of both the A domain size and the distance between the domains. The corresponding increases in the aggregation number and extension of bottlebrush strands lead to a significant increase of the strain‐stiffening parameter up to 0.7, approaching values characteristic of the brain and skin tissues. Network reconfiguration without disassembly is an efficient approach to adjusting the mechanical performance of tissue‐mimetic materials to meet the needs of diverse biomedical applications. 

Abstract Hydrogels are explored for applications in agriculture, water purification, and biomedicine, leveraging softness, elasticity, and high water uptake. However, hydrogels are notoriously brittle, especially at high water content. This shortcoming puts the improvement of hydrogel mechanics at the forefront of current research. Yet modern strategies for enhancing gel resilience come at the expense of softness and swelling. This problem is addressed using bottlebrush networks with disentangled strands and hidden length reservoirs, which synergistically enhance gel swelling and robustness while maintaining their softness. Implementing a facile one‐pot synthesis of single‐stranded bottlebrush networks with a relatively hydrophobic poly(2‐hydroxyethyl methacrylate) (PHEMA) backbone and hydrophilic poly(2‐methyl‐2‐oxazoline) (PMOx) side chains, hydrogels are prepared with a modulus below <1 kPa and swelling ratios up to 125 that can withstand up to 10‐fold extension. 

Heterogeneous photocatalysts (PCs) have garnered attention for their sustainability and cost-effectiveness. 

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.