Multifunctional low-carbon LC3–PPy cementitious composite for integrated structural sensing and electrochemical energy storage

Abstract

Abstract

A multifunctional low-carbon Limestone–Calcined Clay–Cement (LC 3 )-based composite incorporating polypyrrole (PPy) was developed to integrate piezoresistive self-sensing and electrochemical energy storage within a cementitious system. The binder was formulated by replacing 40% of cement with LC 3 , while PPy was synthesized via dopant-assisted oxidative polymerization and incorporated at 0–2.0 vol%. Mechanical, microstructural, and durability characteristics were evaluated through comprehensive testing. Electromechanical behavior was assessed under cyclic loading (30–80% f'c) across varying relative humidity (∼40–95%), followed by the development of a machine-learning framework for predicting stress, strain, and damage index from electrical response. Electrochemical performance was characterized using cyclic voltammetry, galvanostatic charge–discharge, and electrochemical impedance spectroscopy. An optimal composition (LC 3 –PPy1.0) exhibited enhanced multifunctional performance, achieving compressive strength of ∼47 MPa and improved durability under aggressive conditions. The composite demonstrated stable and sensitive self-sensing behavior, with fractional change in electrical resistance of ∼±6% at 30% fc and ∼±25% at 80% fc, and strong correlation with damage evolution. Electrical sensitivity increased with relative humidity due to coupled ionic–electronic conduction. The machine-learning model achieved high predictive accuracy (R 2 ≈0.98 for stress/strain and ≈0.93 for damage). Electrochemically, PPy increased areal capacitance from ∼2 to∼53 mF cm -2 and reduced charge transfer resistance by∼75%, confirming effective pseudocapacitive behavior. These results establish LC 3 –PPy composites as multifunctional low-carbon materials for integrated sensing and energy storage in structural components. The combination of self-sensing and electrochemical energy-storage capabilities provides a foundation for future self-powered structural health monitoring systems, with the potential to reduce external wiring and power requirements in smart infrastructure. Complete experimental, modelling, and Python datasets are provided in the Appendices for reproducibility.