Multifunctional low-carbon LC3–PPy cementitious composite for integrated structural sensing and electrochemical energy storage
構造センシングと電気化学的エネルギー貯蔵を統合した多機能低炭素LC3-PPyセメント複合材料 (AI 翻訳)
Mohammadmahdi Abedi, Zivar Azmoodeh, Eloi Figueiredo
🤖 gxceed AI 要約
日本語
本研究では、セメントの40%を石灰石-焼成粘土-セメント(LC3)で代替した低炭素セメント系複合材料に、ポリピロール(PPy)を添加し、圧電抵抗自己センシングと電気化学エネルギー貯蔵を統合した。最適組成(LC3-PPy1.0)は約47MPaの圧縮強度を示し、機械学習モデルにより応力、ひずみ、損傷指数を高精度(R²≈0.98, 0.93)で予測可能。また、面積容量が約2から53mF/cm²に向上し、自己センシングとエネルギー貯蔵の両機能を実現した。
English
This study developed a low-carbon cement composite replacing 40% of cement with Limestone-Calcined Clay-Cement (LC3) and incorporating polypyrrole (PPy) for integrated piezoresistive self-sensing and electrochemical energy storage. The optimal composition (LC3-PPy1.0) achieved ~47 MPa compressive strength, and a machine-learning model accurately predicted stress, strain, and damage index (R²≈0.98 and 0.93). Electrochemically, areal capacitance increased from ~2 to ~53 mF/cm², enabling dual functionality in structural components.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は、日本でも注目される低炭素建材の技術開発に寄与するが、GX開示やSSBJなどの制度面との直接的関連は薄い。日本の建設・インフラ分野での脱炭素化に向けた材料選択肢として参考になる。
In the global GX context
This paper contributes to global low-carbon construction materials with smart functionalities, relevant for infrastructure decarbonization. It does not directly address climate disclosure frameworks like TCFD or ISSB but offers a technical foundation for sustainable building practices.
👥 読者別の含意
🔬研究者:Provides a novel approach to combining self-sensing and energy storage in low-carbon cementitious materials, with machine learning for damage prediction.
🏢実務担当者:Could be used for developing smart infrastructure with reduced carbon footprint and self-powered monitoring capabilities.
🏛政策担当者:Highlights potential for low-carbon construction materials to enhance building sustainability and resilience.
📄 Abstract(原文)
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.jpcs.2026.113990first seen 2026-07-18 05:37:26
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