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Three-dimensional bioprinting has emerged as a promising tool for spatially patterning cells to fabricate models of human tissue. Here, we present an engineered bioink material designed to have viscoelastic mechanical behavior, similar to that of living tissue. This viscoelastic bioink is cross-linked through dynamic covalent bonds, a reversible bond type that allows for cellular remodeling over time. Viscoelastic materials are challenging to use as inks, as one must tune the kinetics of the dynamic cross-links to allow for both extrudability and long-term stability. We overcome this challenge through the use of small molecule catalysts and competitors that temporarily modulate the cross-linking kinetics and degree of network formation. These inks were then used to print a model of breast cancer cell invasion, where the inclusion of dynamic cross-links was found to be required for the formation of invasive protrusions. Together, we demonstrate the power of engineered, dynamic bioinks to recapitulate the native cellular microenvironment for disease modeling.

Data‐driven, machine learning (ML)‐assisted approaches have been used to study structure‐property relationships at the atomic scale, which have greatly accelerated the screening process and new material discovery. However, such approaches are not easily applicable to modulating material properties of a soft material in a laboratory with specific ingredients. Moreover, it is desirable to relate material properties directly to the experimental recipes. Herein, a data‐driven approach to tailoring mechanical properties of a soft material is demonstrated using ML‐assisted predictions of mechanical properties based on experimental synthetic recipes. Polyurethane (PU) elastomer is used as a model soft material to demonstrate the approach and experimentally varied mechanical properties of the PU elastomer by modulating the mixing ratio between components of the elastomer. Twenty‐five experimental conditions are selected based on the design of experiment and use those data points to train a linear regression model. The resulting model takes desired mechanical properties as input and returns synthetic recipes of a soft material, which is subsequently validated by experiments. Lastly, the prediction accuracies of different machine learning algorithms is compared. It is believed that the approach is widely applicable to other material systems to establish experimental conditions and material property relationships for soft materials.

Ring‐opening metathesis polymerization (ROMP) has become one of the most important living polymerizations. Cyclopropenes (CPEs) remain underexplored for ROMP. Described here is that the simple swap of 1‐methyl to 1‐phenyl on 1‐(benzoyloxymethyl)CPEs elicited strikingly different modes of reactivity, switching from living polymerization to either selective single‐addition or living alternating ROMP. The distinct reactivity stems from differences in steric repulsions at the Ru alkylidene after CPE ring opening. Possible olefin or oxygen chelation from ring‐opened CPE substituents was also observed to significantly affect the rate of propagation. These results demonstrate the versatility of CPEs as a new class of monomers for ROMP, provide mechanistic insights for designing new monomers with rare single‐addition reactivity, and generate a new functionalizable alternating copolymer scaffold with controlled molecular weight and low dispersity.

Ring‐opening metathesis polymerization (ROMP) has become one of the most important living polymerizations. Cyclopropenes (CPEs) remain underexplored for ROMP. Described here is that the simple swap of 1‐methyl to 1‐phenyl on 1‐(benzoyloxymethyl)CPEs elicited strikingly different modes of reactivity, switching from living polymerization to either selective single‐addition or living alternating ROMP. The distinct reactivity stems from differences in steric repulsions at the Ru alkylidene after CPE ring opening. Possible olefin or oxygen chelation from ring‐opened CPE substituents was also observed to significantly affect the rate of propagation. These results demonstrate the versatility of CPEs as a new class of monomers for ROMP, provide mechanistic insights for designing new monomers with rare single‐addition reactivity, and generate a new functionalizable alternating copolymer scaffold with controlled molecular weight and low dispersity.