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Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers
(2024)
Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%.
This study focuses on the corrosion mechanism of carbon steel exposed to an artificial geothermal brine influenced by carbon dioxide (CO2) gas. The tested brine simulates a geothermal source in Sibayak, Indonesia, containing 1500 mg/L of Cl-, 20 mg/L of SO4 2-, and 15 mg/L of HCO3-with pH 4. To reveal the temperature effect on the corrosion behavior of carbon steel, exposure and electrochemical tests were carried out at 70 °C and 150 °C. Surface analysis of corroded specimens showed localized corrosion at both temperatures, despite the formation of corrosion products on the surface. After 7 days at 150 °C, SEM images showed the formation of an adherent, dense, and crystalline FeCO3 layer. Whereas at 70 °C, the corrosion products consisted of chukanovite (Fe2(OH)2CO3) and siderite (FeCO3), which are less dense and less protective than that at 150 °C.
Control experiments under Ar-environment were used to investigate the corrosive effect of CO2. Free corrosion potential (Ecorr) and electrochemical impedance spectroscopy (EIS) confirm that at both temperatures, the corrosive effect of CO2 was more significant compared to that measured in the Ar-containing solution. In terms of temperature effect, carbon steel remained active at 70 °C, while at 150 °C, it became passive due to the FeCO3 formation. These results suggest that carbon steel is more susceptible to corrosion at the near ground surface of a geothermal well, whereas at a deeper well with a higher temperature, there is a possible risk of scaling (FeCO3 layer). A longer exposure test at 150 °C with a stagnant solution for 28 days, however, showed the unstable FeCO3 layer and therefore a deeper localized corrosion compared to that of seven-day exposed specimens.