Performance and predictive modeling of surface carbonized plant fiber reinforced alkali-activated self-insulating mortar

Incorporating stalk fibers into alkali-activated cement faces challenges: alkaline exposure leaches sugars, while their smooth, low-energy surfaces weaken interfacial bonding. To address these issues, this study focuses on the surface carbonization of stalk fibers. The microstructural characteristics of surface-carbonized stalk were systematically investigated through multiple characterization methodologies. This research thoroughly examines the effects of carbonization temperature and duration on both mechanical properties and thermal transmission characteristics of alkali-activated cement-based composites, while conducting comparative analyses of different surface modification strategies for performance enhancement in stalk-reinforced systems. Experimental findings demonstrate that surface carbonization under standard curing conditions outperforms NaOH immersion treatment, with the resultant mortar exhibiting impressive mechanical properties surpassing those of untreated stalk composites. Notably, this composite material simultaneously achieves enhanced thermal insulation performance, manifesting a thermal conductivity coefficient of 0.2373 W/m<sup>2</sup> K. Furthermore, a machine learning framework was developed to model the relationship between modified stalk microstructure and composite mechanical properties, enabling accurate prediction of material performance.

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Publication Details

Journal
Scientific Reports
Published
2025-04-21
DOI
https://doi.org/10.1038/s41598-025-98244-0
Primary Topic
Concrete and Cement Materials Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Performance and predictive modeling of surface carbonized plant fiber reinforced alkali-activated self-insulating mortar

Jingyao Li, Shibo Wang, Ming Gao, Shuang Song
Scientific Reports
Concrete and Cement Materials Research
article

Performance and predictive modeling of surface carbonized plant fiber reinforced alkali-activated self-insulating mortar

Jingyao Li, Shibo Wang, Ming Gao, Shuang Song
article en

Abstract

Incorporating stalk fibers into alkali-activated cement faces challenges: alkaline exposure leaches sugars, while their smooth, low-energy surfaces weaken interfacial bonding. To address these issues, this study focuses on the surface carbonization of stalk fibers. The microstructural characteristics of surface-carbonized stalk were systematically investigated through multiple characterization methodologies. This research thoroughly examines the effects of carbonization temperature and duration on both mechanical properties and thermal transmission characteristics of alkali-activated cement-based composites, while conducting comparative analyses of different surface modification strategies for performance enhancement in stalk-reinforced systems. Experimental findings demonstrate that surface carbonization under standard curing conditions outperforms NaOH immersion treatment, with the resultant mortar exhibiting impressive mechanical properties surpassing those of untreated stalk composites. Notably, this composite material simultaneously achieves enhanced thermal insulation performance, manifesting a thermal conductivity coefficient of 0.2373 W/m<sup>2</sup> K. Furthermore, a machine learning framework was developed to model the relationship between modified stalk microstructure and composite mechanical properties, enabling accurate prediction of material performance.

Scientific ReportsVol. 15(1)
Jilin University (CN), Jilin Jianzhu University (CN), Health Commission of Jilin Province (CN), Jilin Electric Power Research Institute (China) (CN)
Natural Science Foundation of Jilin Province, People's Government of Jilin Province, Jilin University
Openalex Percentile: Top 92%
Concrete and Cement Materials Research
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Performance and predictive modeling of surface carbonized plant fiber reinforced alkali-activated self-insulating mortar — Jingyao Li, Shibo Wang, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS