Real-time in-situ coatings corrosion monitoring using machine learning-enhanced triboelectric nanogenerator
Year: 2024
Type: article
Abstract: Current methods for monitoring coating corrosion are limited by their inability to provide real-time data and dependence on external power sources. This study presents a novel in-situ corrosion monitoring system using a solid-liquid triboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for self-powered sensing. TENG signals and electrochemical impedance spectra were measured on a dopamine-modified lignin-polydimethylsiloxane coating on steel in 1 M NaCl solution under no corrosion, indentation, pitting, and broken conditions, respectively. We extract time-frequency features from the TENG signals to predict the coating’s corrosion condition by applying a customised convolutional neural network (CNN). By extracting time-frequency features from the TENG signals and applying a custom CNN, a prediction accuracy of 99 % for corrosion classification was achieved. Furthermore, the CNN regression model predicted coating impedance values with a high coefficient of determination (R² = 0.98), demonstrating its effectiveness in tracking corrosion progression. The developed TENG also facilitates defect localisation via a matrix electrode beneath the coating. Our approach introduces a promising real-time technology for in-situ corrosion monitoring. • A new real-time corrosion monitoring system was presented using a self-powered triboelectric nanogenerator (TENG). • CNN classification model using TENG current signals achieved 99 % accuracy in identifying corrosion severity. • CNN regression model accurately predicts coating impedance values during corrosion, with a high R² of 0.98. • TENG signals could indicate damage precisely and are feasible for defect localisation.
Source: Sensors and Actuators A Physical
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,540
Awards: Lignin-vegetable oil oleogels toward fully green, sustainable, self-healing, wear resistant, anti-corrosion and anti-fouling coating, G1635938757, G1824486919, Lignin-based lubricants for sustainable society, Lignin based green lubricant additives enabled by reciprocal hydrogen bonding, Lignin-based coatings: A novel approach to turn challenges into opportunities for anti-corrosion and anti-wear applications, Intelligent on-line lubricants condition monitoring enabled by durable liquid-solid triboelectric nanogenerator, G5090217880, G6472157092, Lignin-amino acid based new green lubricants enabled by reciprocal hydrogen bonding, G7334277825, G7664985397, G8109448470
OpenAlex ID: W4403471520