Regulating bacterial behavior within hydrogels of tunable viscoelasticity

Engineered living materials (ELMs) are a new class of materials in which living organisms incorporated into diffusive matrices uptake a fundamental role in material composition and function. Understanding how the spatial confinement in 3D affects the behavior of the embedded cells is crucial to design and predict ELM functions, regulate and minimize their environmental impact and facilitate their translation into applied materials. This study investigates the growth and metabolic activity of bacteria within an associative hydrogel network (Pluronic-based) with mechanical properties that can be tuned by introducing a variable degree of acrylate crosslinks. Individual bacteria distributed in the hydrogel matrix at low density form functional colonies whose size is controlled by the extent of permanent crosslinks. With increasing stiffness and decreasing plasticity of the matrix, a decrease in colony volumes and an increase in their sphericity is observed. Protein production surprisingly follows a different pattern with higher production yields occurring in networks with intermediate permanent crosslinking degrees. These results demonstrate that, bacterial mechanosensitivity can be used to control and regulate the composition and function of ELMs by thoughtful design of the encapsulating matrix, and by following design criteria with interesting similarities to those developed for 3D culture of mammalian cells.

New Hydrogel Network Based on Alginate and a Spiroacetal Copolymer

This study reports a strategy for developing a biohybrid complex based on a natural/synthetic polymer conjugate as a gel-type structure. Coupling synthetic polymers with natural compounds represents an important approach to generating gels with superior properties and with potential for biomedical applications. The study presents the preparation of hybrid gels with tunable characteristics by using a spiroacetal polymer and alginate as co-partners in different ratios. The new network formation was tested, and the structure was confirmed by FTIR and SEM techniques. The physical properties of the new gels, namely their thermal stability and swelling behavior, were investigated. The study showed that the increase in alginate content caused a smooth increase in thermal stability due to the additional crosslinking bridges that appeared. Moreover, increasing the content of the synthetic polymer in the structure of the gel network ensures a slower release of carvacrol, the encapsulated bioactive compound.

Carbon Nanotubes Hybrid Hydrogels for Environmental Remediation: Evaluation of Adsorption Efficiency under Electric Field

The performance of Carbon Nanotubes hybrid hydrogels for environmental remediation was investigated using Methylene Blue (MB), Rhodamine B (RD), and Bengal Rose (BR) as model contaminating dyes. An acrylate hydrogel network with incorporated CNT was synthesized by photo-polymerization without any preliminary derivatization of CNT surface. Thermodynamics, isothermal and kinetic studies showed favorable sorption processes with the application of an external 12 V electric field found to be able to influence the amount of adsorbed dyes: stronger interactions with cationic MB molecules ( and of 19.72 and 33.45 mg g−1, respectively) and reduced affinity for anionic RD ( and of 28.93 and 13.06 mg g−1, respectively) and neutral BR ( and of 36.75 and 15.85 mg g−1, respectively) molecules were recorded. The influence of pH variation on dyes adsorption was finally highlighted by reusability studies, with the negligible variation of adsorption capacity after five repeated sorption cycles claiming for the suitability of the proposed systems as effective sorbent for wastewater treatment.

Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

Hydrogel has a complex network structure with inhomogeneous and random distribution of polymer chains. Much effort has been paid to fully understand the relationship between mesoscopic network structure and macroscopic mechanical properties of hydrogels. In this paper, we develop a deep learning approach to predict the mechanical properties of hydrogels from polymer network structures. First, network structural models of hydrogels are constructed from mesoscopic scale using self-avoiding walk method. The constructed model is similar to the real hydrogel network. Then, two deep learning models are proposed to capture the nonlinear mapping from mesoscopic hydrogel network structural model to its macroscale mechanical property. A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels under uniaxial tension. Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates that the deep learning models are able to capture the internal relationship between complex network structures and mechanical properties. We hope this approach can provide guidance to structural design and material property design of different soft materials.

Tough and Resilient Hydrogels Enabled by a Multifunctional Initiating and Cross-Linking Agent

Many high-strength hydrogels have been developed in recent years; however, few of them are both tough and resilient, and their intrinsic paradoxical nature makes designing a gel with both high toughness and high resilience a great challenge. To address this problem, we introduced both N,N,N,N-pentamethyldiethylenetriamine (PA) and N,N-methylenebisacrylamide (MBA) into polyacrylamide hydrogel networks to construct an entangled network that contains chemically cross-linked chains and branched chains simultaneously. The entanglements of branched chains can act as a physical cross-linking point to uniformly disperse stress on molecular chains, and chemical cross-linking ensures the stability of the hydrogel network. The increase in the number and length of branched chains is able to achieve an enhancement in strength while the slip of the entangled polymer chains can effectively achieve energy dissipation and can improve the toughness of the gel. Moreover, the resultant hydrogels exhibit an excellent resilience (>98%). Therefore, high toughness and resilience are achieved simultaneously. In addition, we also investigated the initiation mechanism of PA. This strategy creates a new way for the preparation of next-generation high toughness and high resilience hydrogel-based materials, which have promising applications in wearable, flexible strain/pressure sensors.

Injectable and In Situ Gelling Dextran Derivatives Containing Hydrolyzable Groups for the Delivery of Large Molecules

Recently, we reported the synthesis and characterization of a new dextran derivative obtained by grafting polyethylene glycol methacrylate to a polysaccharide backbone through a carbonate bond. This moiety was introduced because it allows for the fabrication, through a photo-induced crosslinking reaction, of biodegradable hydrogels particularly suitable for the release of high molecular weight molecules. Here, we investigate the influence of the oxyethylene chain length and the molecular weight of the starting dextran on the main properties of the polymeric solutions as well as those of the corresponding hydrogels. All synthesized polymeric derivatives were characterized by FTIR, NMR, and rheological analyses. The photo-crosslinking reaction of the polymers allowed us to obtain biodegradable networks tested for their mechanical properties, swelling, and degradation behavior. The results showed that both the oxyethylene chain length as well as the molecular weight of the starting dextran influenced swelling and degradation of the hydrogel network. As a consequence, the different behaviors in terms of swelling and degradability were able to affect the release of a large model molecule over time, making these matrices suitable candidates for the delivery of high molecular weight drug substances.