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Aqueous two-phase systems (ATPS) are useful in various applications, from purification and separation of biomolecules to wastewater treatment. While they have great utility on their own, there is great interest in discovering how their emulsions, comprising droplets of one aqueous phase dispersed in the other aqueous phase, might be stabilized to enhance their functionality and applications. There are several examples of these systems, but the two most common systems found in the literature are PEG–dextran and complex coacervate ATPS. In this Review, we discuss these systems, their utility, and many different approaches for stabilizing their water/water (w/w) emulsions. We highlight examples wherein interfacial stabilizers such as liposomes, polymers of diverse architectures, colloids of varied shapes and morpholo- gies, and even whole cells have been employed. These stabilization approaches for both PEG–dextran and complex coacervate ATPS are discussed. We conclude with a discussion of the applications of these ATPS and how they can benefit from the creation of corresponding w/w emulsions with stabilized droplets. 

Dynamic light scattering (DLS) is a commonly used analytical tool for characterizing the size distribution of colloids in a dispersion or a solution. Typically, the intensity of a scattering produced from the sample at a fixed angle from an incident laser beam is recorded as a function of time and converted into time autocorrelation data, which can be inverted to estimate the distribution of colloid diffusivity to estimate the colloid size distribution. For polydisperse samples, this inversion problem, being a Fredholm integral equation of the first kind, is ill-posed and is typically handled using cumulant expansions or regularization methods. Here, we introduce a user-friendly graphical user interface (GUI) for analyzing the measured scattering intensity time autocorrelation data using both the cumulant expansion method and regularization methods, with the latter implemented using various commonly employed algorithms, including NNLS, CONTIN, REPES, and DYNALS. The GUI allows the user to modulate any and all of the fit parameters, offering extreme flexibility. Additionally, the GUI also enables a comparison of the size distributions generated by various algorithms and an evaluation of their performance. We present the fit results obtained from the GUI for model monomodal and bimodal dispersions to highlight the strengths, limitations, and scope of applicability of these algorithms for analyzing time autocorrelation data from DLS. 

Photocrosslinkable precursors (small molecules or polymers) undergo rapid crosslinking upon photoirradiation, forming covalently crosslinked hydrogels. The spatiotemporally controlled crosslinking, which can be achieved in situ , encourages the utility of photocrosslinked hydrogels in biomedicine as bioadhesives, bioprinting inks, and extracellular matrix mimics. However, the low viscosity of the precursor solutions results in unwanted flows and dilution, leading to handling difficulties and compromised strength of the photocrosslinked hydrogels. Here, we introduce oppositely charged triblock polyelectrolytes as additives for precursor solutions that transform them into self-assembled polyelectrolyte complex (PEC) hydrogels with enhanced shear strength and viscosity, providing interim protection against precursor dilution and mitigating secondary flows. The PEC network also augments the properties of the photocrosslinked hydrogels. Crosslinking of the precursors upon photoirradiation results in the formation of interpenetrating polymer network hydrogels with PEC and covalently-linked networks that exhibit shear moduli exceeding the linear combination of the moduli of the constituent networks and overcome the tensile strength–extensibility tradeoff that restricts the performance of covalently-linked hydrogels. The reinforcement approach is shown to be compatible with four types of photocrosslinkable precursors, does not require any modification of the precursors, and introduces minimal processing steps, paving the way for a broader translation of photocrosslinkable materials for biomedical applications. 

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. 

Ultrathin foam films containing supramolecular structures like micelles in bulk and adsorbed surfactant at the liquid–air interface undergo drainage via stratification. At a fixed surfactant concentration, the stepwise decrease in the average film thickness of a stratifying micellar film yields a characteristic step size that also describes the quantized thickness difference between coexisting thick–thin flat regions. Even though many published studies claim that step size equals intermicellar distance obtained using scattering from bulk solutions, we found no reports of a direct comparison between the two length scales. It is well established that step size is inversely proportional to the cubic root of surfactant concentration but cannot be estimated by adding micelle size to Debye length, as the latter is inversely proportional to the square root of surfactant concentration. In this contribution, we contrast the step size obtained from analysis of nanoscopic thickness variations and transitions in stratifying foam films using Interferometry Digital Imaging Optical Microscopy (IDIOM) protocols, that we developed, with the intermicellar distance obtained using small-angle X-ray scattering. We find that stratification driven by the confinement-induced layering of micelles within the liquid–air interfaces of a foam film provides a sensitive probe of non-DLVO (Derjaguin–Landau–Verwey–Overbeek) supramolecular oscillatory structural forces and micellar interactions.