6 research outputs found

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

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    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

    Viscous and Viscoelastic Fingering Instabilities

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    The growth of interfacial instabilities gives rise to many complex patterns observed in nature and our daily life. Viscous fingering, created at the fluid-fluid interface when a higher viscosity fluid is displaced by another less viscous fluid, provides a quintessential example of such instabilities and model system for investigating coating instabilities. The instabilities and pattern formation associated with fingering are most well-studied for pairs of Newtonian fluids, where a single parameter, the ratio of viscosity of inner to outer fluid, plays the role of control parameter. However, characterizing fingering instabilities for complex fluids, including polymer solutions, remains a challenge because most complex fluids display flow kinematics and instabilities that are distinct from Newtonian fluids influenced by conjunctional combination of rate-dependent viscosity and elasticity. To decouple these effects, David Boger formulated ‘purely elastic’ fluids by dissolving low amounts of high molecular weight polymer in a relatively high viscosity solvent. As Boger fluids that exhibit rate-independent viscosity, are usually formulated to allow elasticity measurements on torsional rheometer, such fluids are typically too viscous for fingering experiments and emulating coating flows and instabilities. In this dissertation, we used aqueous PEO/PEG and PEO/glycerin Boger fluids as low viscosity, elastic fluids. We characterize distinct influence on fingering onset and growth even though characterizing elasticity using state-of-the-art torsional rheometry is challenging due to elasticity being too low to be measurable, or as it drives elastic instabilities that cause non-viscometric flows. We observe that the PEO-based Boger fluids increase the number of fingers or formed, and influence both onset and growth of instabilities. Here we utilize Dripping-onto-Substrate (DoS) rheometry protocols which rely on the characterization of capillarity-driven pinching dynamics for measurement of relaxation time (elasticity) and extensional viscosity. Thus, we probe and investigate the effects Boger fluids elasticity on fingering instabilities without relying on torsional rheometry for quantifying elastic response evaluation, and also consider effect of extensional rheology response, as streamwise velocity gradients can arise near deforming interface. While the majority of studies using complex fluids focused on the analysis of the critical wavelength or finger width, which describes the pattern selection at the onset, a variety of growth features and length scales that emerge after sufficient time of fingering propagation are usually neglected. Here we carry out a systematic comparison of fingering with viscoelastic and Newtonian fluids as a function of viscosity ratio at the onset and late stages of the instability and characterize the effects of elasticity on the large-scale patterns. We show that dimensionless numbers based on viscoelastic measures obtained using DoS rheometry help in capturing and comparing the influence of viscoelasticity in Hele-Shaw cell flows

    Block Polyelectrolyte Additives Modulate the Viscoelasticity and Enable 3D Printing of Gelatin Inks at Physiological Temperatures

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    We demonstrate the utility of block polyelectrolyte (bPE) additives to enhance viscosity and resolve longstanding challenges with the three-dimensional printability of extrusion-based biopolymer inks. The addition of oppositely charged bPEs into 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 three-dimensional networks) formed by the oppositely charged bPEs, as 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 post-extrusion flow characteristics for 3D bioprinting, including prominent yield stress behavior, strong shear thinning, and rapid recovery upon flow cessation. Moreover, the bPE self-assemblies also contribute to the robustness of the photocrosslinked hydrogels – photocrosslinked GelMA/bPE hydrogels are shown to exhibit higher shear strength than photocrosslinked GelMA hydrogels. We envision this study to serve as a practical guide for the bioprinting of bespoke extrusion inks where bPE are used as scaffolds and viscosity enhancers that can be emulated in a range of biopolymers and photocurable precursors

    Transfer Learning Facilitates the Prediction of Polymer–Surface Adhesion Strength

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    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
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