Tough and Biocompatible Hydrogel Tissue Adhesives Entirely Based on Naturally Derived Ingredients

Hydrogel tissue adhesives have tremendous potential applications in biological engineering. Existing hydrogel tissue adhesives generally do not have adequate mechanical robustness and acceptable biocompatibility at the same time. Herein, we report a one-step method to synthesize tough and biocompatible hydrogel tissue adhesives entirely made of naturally derived ingredients. We select two natural polymers, chitosan and gelatin, to construct the backbone and a bioderived compound, genipin, as the cross-linker. We show that, upon gelation, genipins cross-link chitosan and gelatin to form two interpenetrated networks and interlink them to tissue surfaces. Meanwhile, hydrogen bonds form in the matrix to strengthen the networks and at the interface to strengthen the adhesion between the hydrogel and tissue. Furthermore, we elaborately use high initial polymer contents to induce topological entanglements in the polymer networks to toughen the hydrogel. The resulting chitosan–gelatin hydrogel provides a tough matrix, and the robust covalent interlinks and hydrogen bonds provide a strong interface, achieving a tensile strength of ∼190 kPa, a fracture toughness of 205.7 J/m2, a mode I adhesion energy of 197.6 J/m2, and a mode II adhesion energy of 51.2 J/m2. We demonstrate that the hydrogel tissue adhesive is injectable, degradable, and noncytotoxic and can be used for the controlled release of the anticancer drug cisplatin. All-natural ingredient-based tough and biocompatible hydrogels are promising as tissue adhesives for biomedical and related applications

Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder–decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data