Polymer Gels

Hydropower, a robust clean energy source, inspires this study on capturing underwater kinetic energy using two electroactive polymer (EAP) materials - ionic polymer-metal composites (IPMCs) and electroactive polymer gels (EPG). Modeled after the fish lateral line, a fabricated EAP-based artificial neuromast responds to hydrodynamic stimuli, inducing bending and compressive strain, to leverage the unique MET behavior in both materials. The system, tested in a Brett-type flume with varied flow speeds, demonstrates energy harvesting potential. Mimicking nature’s sensory mechanisms, this artificial neuromast presents a passive, scalable, and eco-friendly means for underwater energy harvesting.

This study explores the development of an artificial lateral line system using electroactive polymer (EAP) sensors, specifically, ionic polymer-metal composites (IPMCs) and polymer gels. A TPU-based canal structure was 3D printed with two embedded surface TPU-DBA polymer gel sensors at each pore entrance, enabling the detection of hydrodynamic pressure changes in the surrounding fluid environment. Additionally, a single IPMC sensor enclosed within a PDMS cupula-like structure was placed in the center of the canal, emulating the cilium structure of the lateral line by operating as a bending mechanoelectrical transducer. The results validate the efficacy of the proposed artificial lateral line model.

There has been a recent research breakthrough in the understanding of the unique mechanoelectrical transduction (MET) abilities of soft polymer gels. These gel polymers, commonly PVC-based, have been widely studied regarding their actuation abilities. As a sensor however, the current mechanism hypothesis has been recently attributed to the phenomenon known as Langmuir adsorption. In this study, an application-based approach is considered to exercise the content of the suggested MET theory. Outcomes of this study include characterization of polymer gel sensors within an aquatic environment, as well as hydrofoil flow visualization via particle imaging velocimetry (PIV) with analogous gel sensor data.