Block Polyelectrolyte Additives That Modulate the Viscoelasticity and Enhance the Printability of Gelatin Inks at Physiological Temperatures.

We demonstrate the utility of block polyelectrolyte (bPE) additives to enhance viscosity and resolve challenges with the three-dimensional (3D) printability of extrusion-based biopolymer inks. The addition of oppositely charged bPEs to solutions of photocurable gelatin methacryloyl (GelMA) results in complexation-driven self-assembly of the bPEs, leading to GelMA/bPE inks that are printable at physiological temperatures, representing stark improvements over GelMA inks that suffer from low viscosity at 37 °C, leading to low printability and poor structural stability. The hierarchical microstructure of the self-assemblies (either jammed micelles or 3D networks) formed by the oppositely charged bPEs, confirmed by small-angle X-ray scattering, is attributed to the enhancements in the shear strength and printability of the GelMA/bPE inks. Varying bPE concentration in the inks is shown to enable tunability of the rheological properties to meet the criteria of pre- and postextrusion flow characteristics for 3D printing, including prominent yielding behavior, strong shear thinning, and rapid recovery upon flow cessation. Moreover, the bPE self-assemblies also contribute to the robustness of the photo-cross-linked hydrogels; photo-cross-linked GelMA/bPE hydrogels are shown to exhibit higher shear strength than photo-cross-linked GelMA hydrogels. Last, the assessment of the printability of GelMA/bPE inks indicates excellent printing performance, including minimal swelling postextrusion, satisfactory retention of the filament shape upon deposition, and satisfactory shape fidelity of the various printed constructs. We envision this study to serve as a practical guide for the printing of bespoke extrusion inks where bPEs are used as scaffolds and viscosity enhancers that can be emulated in a range of photocurable precursors.

Transfer Learning Facilitates the Prediction of Polymer-Surface Adhesion Strength.

Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. ACS Applied Materials & Interfaces 2022, 14, 37161-37169.], ML models were applied to predict the adhesive free energy of polymer-surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive data sets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small data sets by pretraining on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pretrained model facilitates the prediction accuracy significantly on small data sets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design.