As a kind of typical soft and wet material, hydrogel has been increasingly investigated as another way to develop flexible electronics. However, the traditional hydrogel with poor strain and strength performance cannot meet the requirements for stretchable electronics; fabricating a stretchable hydrogel with balanced tensile strength, toughness, and conductivity is still a big challenge. Herein, a new type of physically cross-linked hydrogel with poly(acrylamide-co-acrylic acid)-Fe 3+ and chitosan-SO 4 2− dual ionic networks via facile free radical polymerization and soaking processes is developed to fabricate excellent highperformance flexible sensors. The abundant Fe 3+ and SO 4 2− ions in the hydrogel can not only construct tough and strong dual ionic networks but also give the hydrogel high conductivity. Consequently, the optimal hydrogel possesses high tensile strength (∼5.1 MPa), large strain capacity (∼1225%), elasticity (∼1.13 MPa), high toughness (∼32.1 MJ/m 3 ), and high conductivity (3.04 S/m at f = 0.1M), as well as rapid self-recovery property. Furthermore, the hydrogel conductor has high stretching sensitivity with a gauge factor of 6.0 at strain of 700% and was able to detect conventional motions of the human body such as the motions of the knuckle, speaking, and swallowing, which indicates that our ionic conductive hydrogels can be used to fabricate excellent high-performance flexible sensors.
Hydrogels are widely used in fields such as drug delivery, tissue regeneration, soft robotics and flexible smart electronic devices, yet their application being often limited by unsatisfactory mechanical behaviors. Among...
Considering the nonlinear relationship between variables and fatigue life and the computational burden, a machine learning method integrating the artificial neural network (ANN) and partial least squares (PLS) algorithm was proposed as a framework to identify the genetic features through optimizing fatigue life prediction. Twenty‐seven specimens of 316LN stainless steel under uniaxial and multiaxial loadings were used as examples. As results, early fatigue data were proved to be informative for fatigue life prediction. Moreover, five genetic features were identified out of them, and a predicting model was developed. The predicted fatigue life of these samples using only these five genetic features were all located within the 1.5‐factor band. This framework can be easily extended to identify genetic features and to predict fatigue life of other materials under different loadings. Therefore, it provides an efficient option in this field to greatly reduce experimental time and cost.
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