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

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Yixuan Ma, Zeshi Guo, Jimin Fu, Xiongyu Xi, Pengcheng Ma, Xungai Wang
{"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.

从岩石到纤维:连续岩石纤维的力学特性
连续岩纤维(CRF)的力学性能,特别是其弹性模量和抗拉强度,是这种材料日益增加的应用的基本要求。CRF的研究主要集中在其在纤维增强复合材料中的应用,对纤维结构性能关系的分析较少。本文综述和讨论了目前影响CRF弹性模量和抗拉强度的实验方法、理论、模型和不同生产阶段(地球化学、岩石筛分、熔融、冷却和纤维拉伸)参数。对于目前的研究成果,普遍的争论是网络结构与CRF抗拉强度缺陷之间的权衡。弹性模量的研究是抗拉强度的基础,因为弹性模量的研究可以不考虑某些缺陷,只考虑网络的微观结构、局部原子配位和键合,而弹性模量的研究可以超越缺陷的表征。现有方法的局限性包括晶体和稳定物质的理论,这可能不适用于亚稳单丝或复杂的CRF玻璃。在实验上,在某些会造成永久性损伤的程序中,对纤维进行原位测试是困难的。机器学习(ML)和分子动力学(MD)可以弥补实验数据的不足,减少操作程序的影响,提供基于结构的信息,并反映多个输入特征的综合效果。一种持续的方法应该基于对传统模型的深刻理解,以及对与ML方法相结合的标准化实验和MD数据集的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信