Divide, Conquer, and Stabilize: Engineering Strong Fluid-Fluid Interfaces.

In multiphase materials, structured fluid-fluid interfaces can provide mechanical resistance against destabilization. Coarsening, coalescence, and significant deformation can be stalled with appropriate interfacial rheology and thus preserve interface integrity. Often, interfacial "strength" is generated by dense, packed surface populations, which are challenging to achieve through gradual, equilibrium-limited adsorption. Recent efforts have focused on developing new methods to produce kinetically trapped interfacial structures that possess desirable viscoelasticity or viscoplasticity, sometimes even with sparse populations. In creating these interfaces, we should recognize that the processing history is deterministic and offers alternative handles to engineer useful rheology. In this Perspective, we consider what can be achieved by designing not only the intrinsic qualities of surface-active species but also the process that brings them to the interface. We contrast different classes of processing history through a somewhat historical lens: after creating an interface ("divide"), what ("conquering") strategies exist for populating it with agents that ensure stabilization? Navigating the delicate interplay among property, structure, and processing history is required to improve material and energy use and to realize unique multiphase materials.

Structuring Hydrogel Cross-Link Density Using Hierarchical Filament 3D Printing.

Polymer hydrogels, water-laden 3D cross-linked networks, find broad application as advanced biomaterials and functional materials because of their biocompatibility, stimuli responsiveness, and affordability. The cross-linking density reports material properties such as elasticity, permeability, and swelling propensity. However, this critical design parameter can be challenging to template locally. Here, we report a continuous processing scheme that uses laminar flow to direct the organization of cross-linking density across a single sample. Dilute and concentrated poly(ethylene glycol) diacrylate solutions are fed into custom serpentine millifluidic devices. These feature a modular sequence of splitting, rotation, and recombination elements, which create patterned streamlines that serve as a template for hierarchical concentration distributions. Poly(acrylic acid) microgels impart viscoplasticity, which stabilizes layered flow during multiplication and ensures reliable advection. The devices produce structured, seamless filaments, which are then arranged into objects using 3D printing, and photopolymerized to secure the heterogeneous distribution. The flow-encoded, multiscale architecture provides mechanical contrast, which is demonstratively exploited to program robust and reversible shape transformations, potentially useful in soft actuator and sensor applications. The unique structures achieved, and the geometrically dictated, chemistry-agnostic operating principles used to achieve them, provides a new means to engineer hydrogels to suit a variety of applications.

Engineering Gelation Kinetics in Living Silk Hydrogels by Differential Dynamic Microscopy Microrheology and Machine Learning.

Microbes embedded in hydrogels comprise one form of living material. Discovering formulations that balance potentially competing for mechanical and biological properties in living hydrogels-for example, gel time of the hydrogel formulation and viability of the embedded organisms-can be challenging. In this study, a pipeline is developed to automate the characterization of the gel time of hydrogel formulations. Using this pipeline, living materials comprised of enzymatically crosslinked silk and embedded E. coli-formulated from within a 4D parameter space-are engineered to gel within a pre-selected timeframe. Gelation time is estimated using a novel adaptation of microrheology analysis using differential dynamic microscopy (DDM). In order to expedite the discovery of gelation regime boundaries, Bayesian machine learning models are deployed with optimal decision-making under uncertainty. The rate of learning is observed to vary between artificial intelligence (AI)-assisted planning and human planning, with the fastest rate occurring during AI-assisted planning following a round of human planning. For a subset of formulations gelling within a targeted timeframe of 5-15 min, fluorophore production within the embedded cells is substantially similar across treatments, evidencing that gel time can be tuned independent of other material properties-at least over a finite range-while maintaining biological activity.

Hydrogen Bonding Strength Determines Water Diffusivity in Polymer Ionogels.

Polymeric ionogels, cross-linked gels swollen by ionic liquids (ILs), are useful vehicles for the release and storage of molecular solutes in separation, delivery, and other applications. Although rapid solute diffusion is often critical for performance, it remains challenging to predict diffusivities across multidimensional composition spaces. Recently, we showed that water (a neutral solute) diffuses through alkyl-methylimidazolium halide ILs by hopping between hydrogen bonding sites on relatively immobile cations. Here, we expand on this activated hopping mechanism in two significant ways. First, we demonstrate that water diffuses through poly(ethylene glycol)diacrylate ionogels via the same mechanism at a reduced rate. Second, we hypothesize that the activation energy barrier can be determined from relatively simple 1H NMR chemical shift measurements of the proton responsible for H-bonding. This relationship enables water's diffusivity in ionogels of this class to be predicted quantitatively, requiring only (1) the composition-dependent diffusivity and Arrhenius behavior of a single IL and (2) 1H NMR spectra of the ionogels of interest. High-throughput microfluidic Fabry-Perot interferometry measurements verify prediction accuracy across a broad formulation space (four ILs, 0 ≤ xH2O ≤ 0.7, 0 ≤ ϕPEGDA ≤ 0.66). The predictive model may expedite IL-material screening; moreover, it intimates a powerful connection between solute mobility and hydrogen bonding and suggests targets for rational design.

A microfluidic model of human brain (µHuB) for assessment of blood brain barrier.

Microfluidic cellular models, commonly referred to as "organs-on-chips," continue to advance the field of bioengineering via the development of accurate and higher throughput models, captivating the essence of living human organs. This class of models can mimic key in vivo features, including shear stresses and cellular architectures, in ways that cannot be realized by traditional two-dimensional in vitro models. Despite such progress, current organ-on-a-chip models are often overly complex, require highly specialized setups and equipment, and lack the ability to easily ascertain temporal and spatial differences in the transport kinetics of compounds translocating across cellular barriers. To address this challenge, we report the development of a three-dimensional human blood brain barrier (BBB) microfluidic model (µHuB) using human cerebral microvascular endothelial cells (hCMEC/D3) and primary human astrocytes within a commercially available microfluidic platform. Within µHuB, hCMEC/D3 monolayers withstood physiologically relevant shear stresses (2.73 dyn/cm2) over a period of 24 hr and formed a complete inner lumen, resembling in vivo blood capillaries. Monolayers within µHuB expressed phenotypical tight junction markers (Claudin-5 and ZO-1), which increased expression after the presence of hemodynamic-like shear stress. Negligible cell injury was observed when the monolayers were cultured statically, conditioned to shear stress, and subjected to nonfluorescent dextran (70 kDa) transport studies. µHuB experienced size-selective permeability of 10 and 70 kDa dextrans similar to other BBB models. However, with the ability to probe temporal and spatial evolution of solute distribution, µHuBs possess the ability to capture the true variability in permeability across a cellular monolayer over time and allow for evaluation of the full breadth of permeabilities that would otherwise be lost using traditional end-point sampling techniques. Overall, the µHuB platform provides a simplified, easy-to-use model to further investigate the complexities of the human BBB in real-time and can be readily adapted to incorporate additional cell types of the neurovascular unit and beyond.

Model of Structured Emulsion Droplet Stability and Reconfigurability.

A model of internally structured emulsion droplets is presented that accounts for the traction forces generated by interfacial tension and the von Mises yield criterion of the internal supporting network. For symmetric droplets, the method calculates the total stress acting on a droplet locally, allowing droplet stability and location of failure to be predicted. It is not regions of high interfacial curvature that prompt droplet reconfiguration, rather regions transitioning from high to low curvature. The model enables the design of emulsion droplet response and reconfigurability to external triggers such as changes in surface tension (surfactant concentration) and temperature.

Oil-in-Water-in-Oil Multinanoemulsions for Templating Complex Nanoparticles.

Complex nanoemulsions involving nanodroplets with a defined inner structure have great potential for encapsulation and templating applications. We report a method to form novel complex oil-in-water-in-oil nanoemulsions using a combination of high-energy processing with mixed nonionic surfactants that simultaneously achieve ultralow interfacial tension and frustrated curvature of the water-oil interface. The method produces multinanoemulsions possessing morphologies resembling water-swollen reverse vesicles with core-shell and multicore-shell morphologies of water in cyclohexane. A combination of macroscopic and microscopic characterization conclusively verifies and quantifies the complex morphologies, which vary systematically and reproducibly with water content for water volume fractions between 0.01 and 0.10. The complex morphologies are stable tens of hours, providing a route for their use as liquid templates for internally structured nanoparticles. As a demonstration, we test the complex nanoemulsions' ability to template complex polymer nanogels.

Dark-field differential dynamic microscopy.

Differential dynamic microscopy (DDM) is an emerging technique to measure the ensemble dynamics of colloidal and complex fluid motion using optical microscopy in systems that would otherwise be difficult to measure using other methods. To date, DDM has successfully been applied to linear space invariant imaging modes including bright-field, fluorescence, confocal, polarised, and phase-contrast microscopy to study diverse dynamic phenomena. In this work, we show for the first time how DDM analysis can be extended to dark-field imaging, i.e. a linear space variant (LSV) imaging mode. Specifically, we present a particle-based framework for describing dynamic image correlations in DDM, and use it to derive a correction to the image structure function obtained by DDM that accounts for scatterers with non-homogeneous intensity distributions as they move within the imaging plane. To validate the analysis, we study the Brownian motion of gold nanoparticles, whose plasmonic structure allows for nanometer-scale particles to be imaged under dark-field illumination, in Newtonian liquids. We find that diffusion coefficients of the nanoparticles can be reliably measured by dark-field DDM, even under optically dense concentrations where analysis via multiple-particle tracking microrheology fails. These results demonstrate the potential for DDM analysis to be applied to linear space variant forms of microscopy, providing access to experimental systems unavailable to other imaging modes.

Temperature-Induced Collapse, and Arrested Collapse, of Anisotropic Endoskeleton Droplets.

Micron-scale rod-shaped droplets with a range of aspect ratios are produced using extrusion of oil containing a soft wax crystal network to permit shape customization. A physical model of the droplet shape stability is developed based on balancing interfacial stresses with the internal crystal network yield stress. The model predicts the mechanical properties required for particular droplet size stability, in a given physicochemical environment, and is tested by microscopic observations of droplets over a range of relevant applied temperatures. The time-dependent response to temperature of individual rods is monitored and used to identify the collapse temperature based on structural yielding. Precise temperature control allows variation of the droplet endoskeleton yield stress and direct determination of the droplet stability as a function of size, by observing the onset of collapse by interfacial compression, and enables validation of the model predictions. Mapping the regions of droplet stability and instability for various-sized droplets yields a basis for designing droplet shapes for multiple applications using easily measured physical variables. The phenomenon of arrested collapse is also explored as a means of transforming simple rod-shaped starting materials into more complex shapes and enhancing adhesion to targeted solid surfaces, enabling exploitation of the hybrid solid-liquid nature of these droplets.

Interfacial stability and shape change of anisotropic endoskeleton droplets.

The delivery of suspended active ingredients to a surface is a central function of numerous commercial cosmetic, drug, and agricultural formulations. Many products use liquid droplets as a delivery vehicle but, because interfacial tension keeps droplets spherical, these materials cannot exploit the benefits of anisotropic shape and shape change offered by solid colloids. In this work, individual droplet manipulation is used to produce viscoelastic droplets that can stably retain non-spherical shapes by balancing the Laplace pressure of the liquid-liquid interface with the elasticity of an internal crystalline network. A stability criterion is developed for idealized spherocylindrical droplets and shown to agree with experimental data for varying droplet size and rheology. Shape change can be induced in the anisotropic droplets by upsetting the balance of droplet interfacial tension and internal rheology. Using dilution to increase the interfacial tension shows that external stimuli can trigger collapse and shape change in these droplets. The droplets wrap around substrates during collapse, improving contact and adhesion. The model is used to develop design criteria for production of droplets with tunable response.

Rapid, high resolution screening of biomaterial hydrogelators by µ2rheology.

A combination of sample manipulation and rheological characterization at the microscale is used to identify the gelation of poly(ethylene glycol)-heparin hydrogels over a wide range of compositions. A microfluidic device produces 50-100 droplet samples, each with a different composition. Multiple particle tracking microrheology is used to measure the rheological state of each sample. This combination requires little material and enables efficient and rapid screening of gelation conditions. The high resolution data identifies the gelation reaction percolation boundaries and a lower limit of the total hydrogelator concentration for gelation to occur, which can be used for the subsequent engineering, testing, and processing of these materials.