Coatings are widely applied in industry and life since they safeguard materials from wear and corrosion, particularly in harsh environments. However, the application of traditional coatings always faces two challenges: (1) many traditional coatings are fossil-based and may have additives that are not environmentally friendly, and (2) the effectiveness of these coatings diminishes over time due to wear and corrosion. The environmental impact of coatings, particularly those designed for anti-corrosion and anti-wear, is a significant issue. Sustainable alternatives to traditional approaches are desirable and essential to grapple with environmental degradation and climate change. In addition, current methods for coating wear monitoring present several limitations. The requirement of complex external equipment or additional coating preparation steps increases the economic cost and limits the practical applicability of these monitoring systems in remote or harsh environments where deploying sophisticated equipment may not be feasible.

In order to meet these challenges, the need for real-time wear monitoring methods and the development of sustainable materials based coating for corrosion and wear resistance are thus pressing concerns in machine elements, material science, and engineering. This work aims to embed lignin additives in coatings, optimising the lignin coating to achieve long-term corrosion resistance with improved wear resistance. Meanwhile, this research aims to monitor the coating’s corrosion and wear condition using TENG devices. The research opens possibilities for establishing in-situ and real-time coating condition monitoring.

This thesis used lignin as the sustainable additive to achieve the coating with corrosion and wear resistance. Physical mixing and chemical grafting were two lignin modification methods. The assessment of coatings’ barrier properties and nanoscale wear resistance was characterised by EIS and nano scratch. A novel in-situ coating wear monitoring method based on a solid-liquid triboelectric nanogenerator (TENG) was introduced for coatings’ condition monitoring. A machine learning model was built and trained to deconvolute the TENG signal to predict the coating’s condition. The mechanism of using TENG for coating wear, corrosion and defect conditions monitoring was studied.

Chemically grafted lignin with 15 wt% lignin had an impedance that remained at 10⁵ Ω∙cm² after being immersed in 1 M NaCl solution for a week. After improved by adding PDMS and DOPA pretreatment, the coated steel maintained a high barrier property (impedance level of 10⁹ Ω∙cm²) in 1 M NaCl for around 100 days, which is much longer than for a commercial gelcoat. The lignin coating showed sufficient wear resistance with a low friction coefficient. TENG showed a corresponding signal after wear, while the CNN model using TENG current signals attained 99% prediction accuracy on the test set for coating corrosion stage classification. 

The main results for this thesis show that chemically grafting lignin as an eco-friendly additive in coating enhanced the performance of wear and corrosion resistance, paving the way for more sustainable coating applications. This self-powered TENG sensor generates relevant signals according to the coating degradation stage. It also eliminates the need for external equipment, offering a more practical solution for real-world applications.