From Rock to Fiber: The Mechanical Properties of Continuous Rock Fibers

Abstract The mechanical properties of continuous rock fiber (CRF), particularly its elastic modulus and tensile strength, are essential requirements for the ever-increasing applications of this material. Studies on CRF have primarily focused on its application in fiber-reinforced composites, with much less emphasis on the analysis of the fiber structure–property relationship. This review summarizes and discusses the current experimental approaches, theories, models, and parameters in different production stages (geochemistry, rock screening, melting, cooling, and fiber drawing) that would affect the elastic modulus and tensile strength of CRF. For the current research results, the general debate is the trade-off between the network structure and defects in the tensile strength of CRF. The study of elastic modulus functions as the fundamental of tensile strength, as the former can be explored regardless of certain defects, only considering the microstructure of the network, local atom coordination and bonding, whereas the latter can be studied beyond characterizing the defects. The limitations of current methods include theories for crystals and stable substances, which may not be applicable to metastable monofilaments or complex CRF glasses. Experimentally, in situ testing is difficult for fibers in certain procedures that cause permanent damage. Machine learning (ML) and molecular dynamics (MD) can compensate for the lack of experimental data, reduce the effects of operational procedures, provide structure-based information, and reflect the combined effects of multiple input features. An ongoing approach should be based on a solid understanding of conventional models and improvements in standardized experimental and MD datasets incorporated with ML methods.

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Publication Details

Journal
Natural Resources Research
Published
2025-04-15
DOI
https://doi.org/10.1007/s11053-025-10483-0
Citations
2
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
2.29

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article

From Rock to Fiber: The Mechanical Properties of Continuous Rock Fibers

Jimin Fu, Xiong-Yu Xi, Peng‐Cheng Ma, Ze‐Shi Guo et al.
2 citations
Natural Resources Research
Drilling and Well Engineering
2.29
article

From Rock to Fiber: The Mechanical Properties of Continuous Rock Fibers

Jimin Fu, Xiong-Yu Xi, Peng‐Cheng Ma, Ze‐Shi Guo, Xungai Wang, Yixuan Ma
article en
2 citations

Abstract

Abstract The mechanical properties of continuous rock fiber (CRF), particularly its elastic modulus and tensile strength, are essential requirements for the ever-increasing applications of this material. Studies on CRF have primarily focused on its application in fiber-reinforced composites, with much less emphasis on the analysis of the fiber structure–property relationship. This review summarizes and discusses the current experimental approaches, theories, models, and parameters in different production stages (geochemistry, rock screening, melting, cooling, and fiber drawing) that would affect the elastic modulus and tensile strength of CRF. For the current research results, the general debate is the trade-off between the network structure and defects in the tensile strength of CRF. The study of elastic modulus functions as the fundamental of tensile strength, as the former can be explored regardless of certain defects, only considering the microstructure of the network, local atom coordination and bonding, whereas the latter can be studied beyond characterizing the defects. The limitations of current methods include theories for crystals and stable substances, which may not be applicable to metastable monofilaments or complex CRF glasses. Experimentally, in situ testing is difficult for fibers in certain procedures that cause permanent damage. Machine learning (ML) and molecular dynamics (MD) can compensate for the lack of experimental data, reduce the effects of operational procedures, provide structure-based information, and reflect the combined effects of multiple input features. An ongoing approach should be based on a solid understanding of conventional models and improvements in standardized experimental and MD datasets incorporated with ML methods.

Natural Resources ResearchVol. 34(4)
Hong Kong Polytechnic University (HK), Xinjiang Technical Institute of Physics & Chemistry (CN), University of Chinese Academy of Sciences (CN)
Hong Kong Polytechnic University
Openalex Percentile: Top 14%
Drilling and Well Engineering
2.29
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