RESUMEN
As a kind of flexible intelligent driving material, ionic polymer-metal composite (IPMC) has attracted the attention of researchers due to its advantages of lightweight, large deformation, and fast response. However, the reciprocating bending of IPMC causes cracks to appear on the surface metal electrode layer and reduces the water uptake (WUP). At the same time, the metal particles are extruded, resulting in an increase in resistivity, which affects the driving performance of the materials. Therefore, in this study, considering the preparation cost, Cu-Pt-IPMC using Pt and Cu as a composite electrode with the self-healing system was prepared by electroless plating and Cu2+ was used as driving ions that can form a reversible circulation system with a copper electrode. The WUP, surface resistivity, and driving performance were tested and analyzed and the surface roughness was characterized by Matlab. The results show that the dendritic interface electrodes (DIEs) appear at the contact interface between the metal electrode and the film, which extend deeper and wider in the film with the increase in the cycles of autocatalytic platinum plating (ACP-Pt), and the output displacement and blocking force of 61.20 mm and 34.26 mN, respectively, have been achieved in the Cu-Pt-IPMC sample after three cycles of ACP-Pt. Based on these analyses, this study proves that the presence of Cu2+ can repair the cracked electrode on the surface of IPMC and reduce the surface electrode resistance, improving the driving performance.
RESUMEN
Ionic polymer-metal composite (IPMC) actuators are one of the most prominent electroactive polymers with expected widespread use in the future. The IPMC bends in response to a small applied electric field as a result of the mobility of cations in the polymer network. This paper proposes a Levenberg-Marquardt algorithm backpropagation neural network (LMA-BPNN) prediction model applicable for Cu/Nafion-based ionic polymer-metal composites to predict the actuation property. The proposed approach takes the dimension ratio (DR) and stimulation voltage as the input layer, displacement and blocking force as the output layer, and trains the LMA-BPNN with the experimental data so as to obtain a mapping relationship between the input and the output and obtain the predicted values of displacement and blocking force. An IPMC actuating system is set up to generate a collection of the IPMC actuating data. Based on the input/output training data, the most suitable structure was found out for the BPNN model to represent the IPMC actuation behavior. After training and verification, a 2-9-3-1 BPNN structure for displacement and a 2-9-4-1 BPNN structure for blocking force indicate that the structure can provide a good reference value for the IPMC. The results showed that the BPNN model based on the LMA could predict the displacement and blocking force of the IPMC. Therefore, this model can become an effective solution for IPMC control applications.
RESUMEN
In this study, Cu-Ionic polymer metal composites (Cu-IPMC) were fabricated using the electroless plating method. The properties of Cu-IPMC in terms of morphology, water loss rate, adhesive force, surface resistance, displacements, and tip forces were evaluated under direct current voltage. In order to understand the relationship between lengths and actuation properties, we developed two static models of displacements and tip forces. The deposited Cu layer is uniform and smooth and contains about 90% by weight of copper, according to the energy-dispersive X-ray spectroscopy (EDS) analysis data obtained. The electrodes adhere well (level of 5B) on the membrane, to ensure a better conductivity and improve the actuation performance. The penetration depth of needle-like electrodes can reach up to around 70 µm, and the structure shows concise without complex branches, to speed up the actuation. Overall the maximum displacement increased as the voltage increased. The applied voltage for the maximum force output is 8-9 V. The root mean square error (RMSE) and determination coefficient (DC) of the displacement and force models are 1.66 and 1.23, 0.96 and 0.86, respectively.
