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To obtain the promising pseudocapacitance of MnO2, the composite hydrogel of MnO2 and polyaniline (PANI) was fabricated using in situ polymerization of aniline hydrochloride in the aqueous solution containing commercial MnO2 nanoparticles and additives. Both scanning electron microscopy and transmission electron microscopy results indicated that the composite hydrogel exhibited a 3-D porous structure, within which MnO2 nanoparticles were uniformly embedded. The investigations of cyclic voltammetry, galvanostatic charge−discharge, and electrochemical impedance spectroscopy demonstrated superior supercapacitor (SC) performance of the hydrogel electrode even with high loading. The electrode with loading of 1.5 mg cm–2 showed a favorable specific capacitance (293 F g–1, 10 mV s–1), which only decreased to 258 F g–1 when the loading of the electrode was seven times higher (10.8 mg cm–2). Furthermore, the hydrogel electrode displayed good cycle stability in the acidic solution (81% capacitance retention after 1000 charge/discharge cycles). The favorable electrochemical performance of the composite hydrogel should be attributed to the fast electron/ion transport and good protection for MnO2 in the 3-D porous structure. Due to the facile one-pot synthesis and optimized nanostructure, it could be expected that MnO2-embedded 3-D porous PANI composite hydrogels have great application in the field of high-performance electrode with high loading for SCs.

The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA) optimization. This study proposed an efficient and interpretable method for calibrating the constitutive parameters with macroscopic mechanical tests. This approach utilized the Bayesian neural network (BNN)-based surrogate-assisted GA (SGA) optimization method to identify a group of constitutive parameters that can reproduce the experimental stress–strain curve and crystallographic orientation by crystal plasticity simulation. The proposed approach was performed on the calibration of typical high-entropy alloy material parameters in two different CP models. The use of the surrogate model reduces the call count of simulation in the parameter searching process and speeds up the calibration significantly. With the help of infill sampling, the accuracy of this optimization method is consistent with the CP simulation and not limited by the accuracy of the surrogate model. Another merit of this method is that the pattern that the BNN surrogate found in the model parameters can be interpreted with its integrated gradients, which helps us to understand the relationship between constitutive parameters and the output mechanical response. The interpretation of BNN can guide further experiment design to decouple particular parameters and add constraints provided by the attached experiment or prior knowledge.