Despite existing in biological systems, developing synthetic polyampholyte (PA) hydrogels constructed by both ionic and metal–ligand bonds remains challenging. Herein, a simple secondary equilibrium approach is proposed to fabricate strong and tough PA hydrogels via the synergy of ionic and metal–ligand bonds. The original PA gels (constructed by ionic bonds) are first dialyzed in multivalent metal‐ion solutions to reach a swelling equilibrium and then moved to deionized water to dialyze excess free ions to achieve a new equilibrium. Through this approach, the original PA gel network can be optimized and eventually constructed by ionic and metal–ligand bonds, enabling a synergistic reinforcement. By selecting different original PA gel systems and diverse multivalent metal‐ions, the proposed approach is proved to be generalizable to fabricate strong and tough PA gels. Additionally, the hydrogels have stable ion‐conductivity even at the water‐equilibrium state, making them promising as strain sensors. The viscoelastic and elastic contributions to the mechanical properties of the hydrogels by a viscoelastic model are also discussed to further understand the strengthening and toughening mechanisms. The proposed strategy is simple but effective for achieving strong and tough PA‐based hydrogels. This study also provides new insights for PA hydrogels in electrolyte environments.

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Hierarchically porous SiO 2 /C hollow microspheres (HPSCHMs) were synthesized by a hydrothermal and NaOH-etching combined route. The adsorption performance of the prepared HPSCHMs was investigated to remove Congo Red (CR) in aqueous solution. The results show that the synthesized composite possesses a hollow microspherical structure with hierarchical pores and a diameter of about 100-200 nm, and its surface area is up to 1154 m 2 g À1. This material exhibits a remarkable adsorption performance for CR in solution, and its maximum adsorption amount for CR can reach up to 2512 mg g À1 . It shows faster adsorption and much higher adsorption capacity than the commercial AC and gAl 2 O 3 samples under the same conditions. The studies of the kinetics and thermodynamics indicate that the adsorption of CR on the PHSCHM sample obeys the pseudo-second order model well and belongs to physisorption. The adsorption activation energy is about 7.72 kJ mol À1 . In view of the hierarchically meso-macroporous structure, large surface area and pore volume, the HPSCHM material could be a promising adsorbent for removal of pollutants, and it could also be used as a catalyst support.

Developing conductive hydrogel‐incorporated strain sensors with high gauge factor (GF) and toughness for wearable applications is challenging. Herein, a facile strategy to fabricate strong, tough polyacrylamide‐co‐acrylic acid [P(AAm‐co‐AAc)] hydrogels via the synergy of fiber and metal‐ligand bonds is proposed. Through the secondary equilibrium approach, the pristine P(AAm‐co‐AAc) gel network is reconstructed with fiber and carboxyl–Zr4+ (Zirconium ion) coordination bonds intertwined over the entire gel matrix, generating a synergistic reinforcement in mechanical properties. The resultant hydrogels display a maximum tensile strength of 8.50 MPa and a fracture energy of 2.75 kJ m−2, which is 1–2 orders of magnitude greater than the original P(AAm‐co‐AAc) gels. It is also experimentally approved that by selecting different natural fibers, multivalent metal ions, and synthetic macromolecules containing carboxyl groups, the proposed approach is effective and can be generalized to fabricate strong, tough gels. Additionally, the electrical properties of obtained gel are evaluated by fabricating it into a stretchable strain sensor for object identification or human motion detection. The results reveal a high GF of 5.07 under a strain smaller 55%. These hydrogels are expected to enable numerous applications in soft robotics or wearable healthcare.

Hydrogels with high mechanical strength, good crack resistance, and good adhesion are highly desirable in various areas, such as soft electronics and wound dressing. Yet, these properties are usually mutually exclusive, so achieving such hydrogels is difficult. Herein, we fabricate a series of strong, tough, and adhesive composite hydrogels from polyampholyte (PA) gel reinforced by nonwoven cellulose-based fiber fabric (CF) via a simple composite strategy. In this strategy, CF could form a good interface with the relatively tough PA gel matrix, providing high load-bearing capability and good crack resistance for the composite gels. The relatively soft, sticky PA gel matrix could also provide a large effective contact area to achieve good adhesion. The effect of CF content on the mechanical and adhesion properties of composite gels is systematically studied. The optimized composite gel possesses 35.2 MPa of Young’s modulus, 4.3 MPa of tensile strength, 8.1 kJ m−2 of tearing energy, 943 kPa of self-adhesive strength, and 1.4 kJ m−2 of self-adhesive energy, which is 22.1, 2.3, 1.8, 6.0, and 4.2 times those of the gel matrix, respectively. The samples could also form good adhesion to diverse substrates. This work opens a simple route for fabricating strong, tough, and adhesive hydrogels.

The utilization of Surface Electromyography (sEMG) is widespread for monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces like the larynx, because regular sEMG electrodes have a stiff structure. In this paper, we develop a stretchable, high-density sEMG electrode array via a layer-by-layer printing and lamination process. The electrode offers a series of excellent human-machine interface features in terms of conformal adhesion to the skin, high electron-to-ionic conductivity (and thus lower contact impedance), prolonged environmental adaptability to resist water evaporation, and epidermal biocompatibility. This makes the electrode more appropriate than commercial electrodes for long-term wearable, high-fidelity recording of sEMG at complicated skin interfaces. Systematic in vivo studies investigate its practical functionality in swallowing activities classification, which is accomplished with high accuracy by decoding sEMG signals from the chin with the integration of an ear-mounted wearable system through machine learning algorithms. The results demonstrate clinical feasibility of the system in swallowing recognition-driven, non-invasive, user-comfortable dysphagia rehabilitation.

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