{"title":"From Rock to Fiber: The Mechanical Properties of Continuous Rock Fibers","authors":"Yixuan Ma, Zeshi Guo, Jimin Fu, Xiongyu Xi, Pengcheng Ma, Xungai Wang","doi":"10.1007/s11053-025-10483-0","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"1 1","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11053-025-10483-0","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.