Article(id=1208361641272525326, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208361635656352181, articleNumber=null, orderNo=18, doi=10.3981/j.issn.1000-7857.2024.12.01749, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1734278400000, receivedDateStr=2024-12-16, revisedDate=1753977600000, revisedDateStr=2025-08-01, acceptedDate=1756656000000, acceptedDateStr=2025-09-01, onlineDate=1766025465553, onlineDateStr=2025-12-18, pubDate=1757692800000, pubDateStr=2025-09-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762358400000, onlineIssueDateStr=2025-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766025465553, creator=13701087609, updateTime=1774079944696, updator=sys-migrate, issue=Issue{id=1208361635656352181, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='17', pageStart='1', pageEnd='144', issueExtLink='null', onlineDate='null', pubDate='1757692800000', pubDateStr='2025-09-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766025464214, creator='13701087609', updateTime=1774330860874, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243196994169189037, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208361635656352181, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243196994169189038, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208361635656352181, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=62, endPage=76, ext={EN=ArticleExt(id=1208361642555982362, articleId=1208361641272525326, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Research progress on the corrosion behavior and mechanisms of high−entropy alloys, columnId=1150494642224591153, journalTitle=Science & Technology Review, columnName=Exclusive, runingTitle=null, highlight=null, articleAbstract=

High−entropy alloys (HEAs) exhibit significant application potential in extreme service environments due to their outstanding overall properties, with corrosion resistance being a critical factor determining their service life and reliability. This review systematically summarizes recent advances in the corrosion behavior and mechanisms of HEAs, highlighting the influence of alloy composition and atomic ratio adjustments on corrosion performance, as well as the effects of thermomechanical processing, such as heat treatment and rolling, on microstructure and passive film characteristics. Studies indicate that compositional design and process optimization can substantially alter the corrosion response and passivation behavior of HEAs, thereby affecting their overall corrosion resistance. Future research should focus on further elucidating localized corrosion mechanisms and the evolution of passivation films, integrating machine learning and multiscale simulations for intelligent alloy design, and establishing comprehensive evaluation frameworks that balance mechanical properties, corrosion resistance, and cost−effectiveness. Collectively, this review provides a systematic overview and reference for the design and application of corrosion−resistant HEAs.

, authors=null, authorsList=Shujun DONG, Qiancheng ZHAO, Hongxu CHENG, Minglei SUN, Xiaochen LIU, Xuefei WANG, Hong LUO, authorCompany=null, correspAuthors=Hong LUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1208361644837683832, articleId=1208361641272525326, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=高熵合金的腐蚀行为及机理研究进展, columnId=1150494642375586098, journalTitle=科技导报, columnName=特色专题, runingTitle=null, highlight=null, articleAbstract=

高熵合金凭借其优异的综合性能在极端服役环境中展现出重要的应用前景,其中耐蚀性是决定其服役寿命与可靠性的关键因素。综述了高熵合金腐蚀行为及机理的研究进展,其中,重点阐述了合金元素组成与原子比调控对腐蚀性能的影响,并讨论了热处理、轧制等热机械加工过程对微观组织及钝化膜特性的调控作用。研究表明,成分设计与工艺优化能够显著改变合金的腐蚀响应与钝化行为,从而影响其耐蚀性能。未来的研究仍需深入揭示局部腐蚀机理与钝化膜演变过程,结合机器学习和多尺度模拟开展智能化设计,同时建立兼顾力学性能、耐蚀性与成本效益的综合性能评价体系。

, authors=

董姝君,硕士研究生,研究方向为机器学习设计高强韧耐蚀多主元合金,电子信箱:

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骆鸿(通信作者),教授,研究方向为先进耐蚀材料的设计和服役安全,电子信箱:
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董姝君,硕士研究生,研究方向为机器学习设计高强韧耐蚀多主元合金,电子信箱:

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Corrosion Science, 2012, 63: 316- 322., articleTitle=Preferred crystallographic pitting corrosion of pure magnesium in Hanks' solution, refAbstract=null), Reference(id=1242144696705823367, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.jallcom.2020.158056, pmid=null, pmcid=null, year=2021, volume=863, issue=null, pageStart=158056, pageEnd=null, url=null, language=null, rfNumber=65, rfOrder=64, authorNames=null, journalName=Journal of Alloys and Compounds, refType=null, unstructuredReference=Parakh A, Vaidya M, Kumar N, et al. Effect of crystal structure and grain size on corrosion properties of AlCoCrFeNi high entropy alloy[J]. Journal of Alloys and Compounds, 2021, 863: 158056., articleTitle=Effect of crystal structure and grain size on corrosion properties of AlCoCrFeNi high entropy alloy, refAbstract=null), Reference(id=1242144696764543624, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.actamat.2018.09.040, pmid=null, pmcid=null, year=2018, volume=161, issue=null, pageStart=338, pageEnd=351, url=null, language=null, rfNumber=66, rfOrder=65, authorNames=null, journalName=Acta Materialia, refType=null, unstructuredReference=Laplanche G, Berglund S, Reinhart C, et al. Phase stability and kinetics of σ−phase precipitation in CrMnFeCoNi high−entropy alloys[J]. Acta Materialia, 2018, 161: 338- 351., articleTitle=Phase stability and kinetics of σ−phase precipitation in CrMnFeCoNi high−entropy alloys, refAbstract=null), Reference(id=1242144696827458185, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.actamat.2019.03.010, pmid=null, pmcid=null, year=2019, volume=170, issue=null, pageStart=109, pageEnd=117, url=null, language=null, rfNumber=67, rfOrder=66, authorNames=null, journalName=Acta Materialia, refType=null, unstructuredReference=Wen C, Zhang Y, Wang C X, et al. Machine learning assisted design of high entropy alloys with desired property[J]. Acta Materialia, 2019, 170: 109- 117., articleTitle=Machine learning assisted design of high entropy alloys with desired property, refAbstract=null), Reference(id=1242144696898761354, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.actamat.2023.118954, pmid=null, pmcid=null, year=2023, volume=252, issue=null, pageStart=118954, pageEnd=null, url=null, language=null, rfNumber=68, rfOrder=67, authorNames=null, journalName=Acta Materialia, refType=null, unstructuredReference=Ren D, Wang C C, Wei X L, et al. Building a quantitative composition−microstructure−property relationship of dual−phase steels via multimodal data mining[J]. 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Acta Materialia, 2024, 270: 119873., articleTitle=Synchronously enhancing the strength, toughness, and stress corrosion resistance of high−end aluminum alloys via interpretable machine learning, refAbstract=null), Reference(id=1242144697037173388, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.actamat.2023.119177, pmid=null, pmcid=null, year=2023, volume=257, issue=null, pageStart=119177, pageEnd=null, url=null, language=null, rfNumber=70, rfOrder=69, authorNames=null, journalName=Acta Materialia, refType=null, unstructuredReference=Roy A, Hussain A, Sharma P, et al. Rapid discovery of high hardness multi−principal−element alloys using a generative adversarial network model[J]. Acta Materialia, 2023, 257: 119177., articleTitle=Rapid discovery of high hardness multi−principal−element alloys using a generative adversarial network model, refAbstract=null), Reference(id=1242144697112670861, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, doi=10.1016/j.electacta.2023.143722, pmid=null, pmcid=null, year=2024, volume=476, issue=null, pageStart=143722, pageEnd=null, url=null, language=null, rfNumber=71, rfOrder=70, authorNames=null, journalName=Electrochimica Acta, refType=null, unstructuredReference=Ozdemir H C, Nazarahari A, Yilmaz B, et al. Machine learning–informed development of high entropy alloys with enhanced corrosion resistance[J]. 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Npj Materials Degradation, 2022, 6: 71., articleTitle=Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys, refAbstract=null)], funds=[Fund(id=1242144689269322305, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, awardId=null, language=CN, fundingSource=国家自然科学基金项目(52361145849, 52371047), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1242144683548291574, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, xref=null, ext=[AuthorCompanyExt(id=1242144683556680183, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, companyId=1242144683548291574, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China), AuthorCompanyExt(id=1242144683569263096, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, companyId=1242144683548291574, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京科技大学新材料技术研究院,北京 100083)])], figs=[ArticleFig(id=1242144687419634215, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=EN, label=null, caption=null, figureFileSmall=okRQPl9kTl+q42RriNbeSQ==, figureFileBig=JDMrM23x2rlaQ/eVPsWVmA==, tableContent=null), ArticleFig(id=1242144687495131690, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=CN, label=图1, caption=Al0.3CrxFeCoNi合金的选区电子衍射图(SAED)

(a)x=0;(b)x=0.5;(c)x=1.0;(d)x=1.5;(e)x=1.7;(f)x=2.0

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(a)Al 2p;(b)Cr 2p3/2;(c)Fe 2p3/2;(d)Ni 2p3/2;(e)Mo 3d;(f)O1s

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(a) Mo0和Mo0.3合金形成的钝化膜中,不同价态元素相对于Al 2p、Cr 2p3/2、Fe 2p3/2、Ni 2p3/2、Mo 3d和O1s峰强度的成分比例;

(b) 钝化膜中Al、Cr、Fe、Ni和Mo元素的原子百分比

, figureFileSmall=CX4K+VBPO7hQOWQ5aZxlgg==, figureFileBig=BaJ/Er4U284XGB6+MzOmFg==, tableContent=null), ArticleFig(id=1242144687843258927, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=EN, label=null, caption=null, figureFileSmall=qCBfOhcLFmUvAaGnurn+LQ==, figureFileBig=4M6A4m64gg2Hhu7WSJ10KA==, tableContent=null), ArticleFig(id=1242144687897784880, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=CN, label=图4, caption=室温下铸态AlxCrFeNi3−xx=0.6,0.8,1.0原子百分比)合金在质量百分比为3.5%的NaCl溶液中的电化学行为, figureFileSmall=qCBfOhcLFmUvAaGnurn+LQ==, figureFileBig=4M6A4m64gg2Hhu7WSJ10KA==, tableContent=null), ArticleFig(id=1242144687964893745, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=EN, label=null, caption=null, figureFileSmall=Qs+7+Bs7NLscJ6dsA52ywg==, figureFileBig=rkdMM4Xa8F6nMmqQAaM+0g==, tableContent=null), ArticleFig(id=1242144688027808306, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=CN, label=图5, caption=在质量百分比为3.5%的NaCl溶液中,动电位极化测试至腐蚀电流密度为5 mA/cm2后铸态高熵合金的表面形貌

(a−b)Al0.6;(c−d)Al0.8;(e−f)Al1.0

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(a)x=0.25;(b)x=0.35;(c)x=0.4;(d)x=0.45;(e)x=0.5;(f)x=0.75 高倍放大后的插图显示凹坑

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(a)不含Cu;(b)Cu0.2和Cu0.4;(c)Cu0.6、Cu0.8和Cu1.0

, figureFileSmall=wqu5lJBo19VfPBuYE49ByQ==, figureFileBig=4ALYniOv3X0FgRnd2E6SFw==, tableContent=null), ArticleFig(id=1242144688333992503, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=EN, label=null, caption=null, figureFileSmall=CV3/fKqpTzRSGryw0wspiA==, figureFileBig=Uenh+QKF1Y3jVM6ZCKdjTg==, tableContent=null), ArticleFig(id=1242144688409489976, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=CN, label=图8, caption=在质量百分比为3.5%的NaCl溶液中循环电位极化后的光学显微镜(OM)和SEM图像

(a1−a2)不含Sn;(b1−b2)Sn0.1;(c1−c2)Sn0.5;(d1−d2)Sn1.0

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(a1−a3)不含Sn;(b1−b3)Sn0.1;(c1−c3)Sn0.5;(d1−d3)Sn1.0

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(a)Bode图;(b)Nyquist图;(c1)A700的拟合EIS数据的等效电路模型;(c2)其他样品的拟合EIS数据的等效电路模型;

(d)极化曲线;(e)不同晶粒尺寸的高熵合金腐蚀机理示意

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(a−b)UFG合金;(c−d)CG合金

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Al0.3CrFeCoNiNbx Rs/
(Ω·cm2
CPEc/
(10−5 Ω−1cm−2sn
n1 Rp/
(105 Ω·cm2
CPEp/
(10−6 Ω−1cm−2sn
n2 Rct /
(104 Ω·cm2
χ2/
(10−4
x = 0.25 1.39 3.85 0.89 2.25 9.74 0.89 1.35 6.43
x = 0.35 1.46 3.40 0.92 2.47 8.69 0.92 2.39 1.02
x = 0.40 1.47 2.73 0.92 4.77 5.71 0.92 2.99 0.92
x = 0.45 1.45 2.49 0.91 7.79 3.85 0.91 4.81 1.15
x = 0.50 1.46 2.77 0.92 4.46 5.14 0.92 2.42 1.22
x = 0.75 1.45 3.74 0.91 2.79 5.85 0.91 1.81 1.15
), ArticleFig(id=1242144689122521664, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208361641272525326, language=CN, label=表1, caption=

Al0.3CrFeCoNiNbx高熵合金EIS数据的电等效电路值

, figureFileSmall=null, figureFileBig=null, tableContent=
Al0.3CrFeCoNiNbx Rs/
(Ω·cm2
CPEc/
(10−5 Ω−1cm−2sn
n1 Rp/
(105 Ω·cm2
CPEp/
(10−6 Ω−1cm−2sn
n2 Rct /
(104 Ω·cm2
χ2/
(10−4
x = 0.25 1.39 3.85 0.89 2.25 9.74 0.89 1.35 6.43
x = 0.35 1.46 3.40 0.92 2.47 8.69 0.92 2.39 1.02
x = 0.40 1.47 2.73 0.92 4.77 5.71 0.92 2.99 0.92
x = 0.45 1.45 2.49 0.91 7.79 3.85 0.91 4.81 1.15
x = 0.50 1.46 2.77 0.92 4.46 5.14 0.92 2.42 1.22
x = 0.75 1.45 3.74 0.91 2.79 5.85 0.91 1.81 1.15
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高熵合金的腐蚀行为及机理研究进展
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董姝君 , 赵前程 , 成红旭 , 孙明磊 , 刘孝辰 , 王雪飞 , 骆鸿 *
科技导报 | 特色专题 2025,43(17): 62-76
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科技导报 |特色专题 2025 , 43 (17) : 62 -76
高熵合金的腐蚀行为及机理研究进展
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董姝君 , 赵前程, 成红旭, 孙明磊, 刘孝辰, 王雪飞, 骆鸿*
作者信息
  • 北京科技大学新材料技术研究院,北京 100083
通讯作者:
骆鸿(通信作者),教授,研究方向为先进耐蚀材料的设计和服役安全,电子信箱:
Research progress on the corrosion behavior and mechanisms of high−entropy alloys
Shujun DONG , Qiancheng ZHAO, Hongxu CHENG, Minglei SUN, Xiaochen LIU, Xuefei WANG, Hong LUO*
Affiliations
  • Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
出版时间: 2025-09-13 doi: 10.3981/j.issn.1000-7857.2024.12.01749
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高熵合金凭借其优异的综合性能在极端服役环境中展现出重要的应用前景,其中耐蚀性是决定其服役寿命与可靠性的关键因素。综述了高熵合金腐蚀行为及机理的研究进展,其中,重点阐述了合金元素组成与原子比调控对腐蚀性能的影响,并讨论了热处理、轧制等热机械加工过程对微观组织及钝化膜特性的调控作用。研究表明,成分设计与工艺优化能够显著改变合金的腐蚀响应与钝化行为,从而影响其耐蚀性能。未来的研究仍需深入揭示局部腐蚀机理与钝化膜演变过程,结合机器学习和多尺度模拟开展智能化设计,同时建立兼顾力学性能、耐蚀性与成本效益的综合性能评价体系。

高熵合金  /  腐蚀行为  /  合金元素  /  微观结构

High−entropy alloys (HEAs) exhibit significant application potential in extreme service environments due to their outstanding overall properties, with corrosion resistance being a critical factor determining their service life and reliability. This review systematically summarizes recent advances in the corrosion behavior and mechanisms of HEAs, highlighting the influence of alloy composition and atomic ratio adjustments on corrosion performance, as well as the effects of thermomechanical processing, such as heat treatment and rolling, on microstructure and passive film characteristics. Studies indicate that compositional design and process optimization can substantially alter the corrosion response and passivation behavior of HEAs, thereby affecting their overall corrosion resistance. Future research should focus on further elucidating localized corrosion mechanisms and the evolution of passivation films, integrating machine learning and multiscale simulations for intelligent alloy design, and establishing comprehensive evaluation frameworks that balance mechanical properties, corrosion resistance, and cost−effectiveness. Collectively, this review provides a systematic overview and reference for the design and application of corrosion−resistant HEAs.

high−entropy alloys  /  corrosion resistance  /  alloying elements  /  microstructure
董姝君, 赵前程, 成红旭, 孙明磊, 刘孝辰, 王雪飞, 骆鸿. 高熵合金的腐蚀行为及机理研究进展. 科技导报, 2025 , 43 (17) : 62 -76 . DOI: 10.3981/j.issn.1000-7857.2024.12.01749
Shujun DONG, Qiancheng ZHAO, Hongxu CHENG, Minglei SUN, Xiaochen LIU, Xuefei WANG, Hong LUO. Research progress on the corrosion behavior and mechanisms of high−entropy alloys[J]. Science & Technology Review, 2025 , 43 (17) : 62 -76 . DOI: 10.3981/j.issn.1000-7857.2024.12.01749
高熵合金是由Yeh等[1]和Cantor等[2]在21世纪初首次提出的一类新型合金类型,其定义基于其独特的合金设计概念。这种合金由5种或5种以上的主元素组成,每种元素的原子百分比为5%~35%。这一设计理念允许高熵合金不必遵循等摩尔或接近等摩尔的成分比例,甚至可以包含微量元素来优化材料的各种性能。此外,另一种对高熵合金的定义是基于理想固溶体的摩尔混合熵,当其混合熵大于1.5 RR为气体常数)时,即可被归类为高熵合金。这一定义突破了传统合金设计的范式,使得合金设计的思路从相图的角落和边缘扩展到了相图的中心区域,增加了合金设计的自由度。由于熵效应,高熵合金易于形成面心立方(FCC)[3]、体心立方(BCC)[4]或密排六方(HCP)[5]结构的简单无序固溶体。高熵合金的独特性能源于其内在的4大核心效应:高熵效应、缓慢扩散效应、晶格畸变及鸡尾酒效应[67]。高熵效应有助于形成简单的固溶体结构并促进元素的均匀分布,从而提升合金的钝化膜稳定性,在一定程度上减弱电偶腐蚀的倾向。缓慢的滞后扩散效应被认为是高熵合金结构稳定性、热稳定性和优异机械性能的原因[89]。这些效应的协同作用使得高熵合金与传统合金相比具有硬度高[10]、耐磨性好[1112]、耐腐蚀性强[13]、强度高[14]等诸多突出性能,引发了材料科学与工程领域对其的广泛关注。
在耐蚀性方面,高熵合金由于其独特的化学组成和微观结构,表现出了优于传统合金的抗腐蚀性能。例如,铬、钼、镍等元素的添加可通过促进钝化膜形成或抑制局部腐蚀而提高耐蚀性。早期,大多数关于高熵合金的研究主要集中在单相FCC合金上,如等原子FeCoNiCrMn(也称为Cantor合金),由于其在低温下具有出色的拉伸延展性和断裂韧性,因此受到了广泛的关注[2, 15]。独特的成分和稳定的单相固溶体微观结构也吸引了大量关于Cantor合金腐蚀的研究,包括晶粒尺寸[1617]、溶液环境[1819]、间隙原子[2021]和异质微观结构[2223]的影响。后来,学者将更多的注意力转移到非等原子高熵合金的腐蚀行为上,并讨论了添加元素对微观结构演变和腐蚀性能的影响[2426]。Wu等[27]开发的新型非等原子比Fe40Ni20Co20Cr20高熵合金不仅展现了优异的机械性能,而且在0.1 mol/L的H2SO4溶液中相较于传统的316L不锈钢和等原子FeMnNiCoCr高熵合金,展示了更加优异的耐蚀性。Shuang等[28]研究了纳米结构共晶高熵合金FeCrNiCoNb0.5在氯离子溶液中的腐蚀行为。实验结果表明,与传统304不锈钢相比,该高熵合金在1 mol/L NaCl溶液中表现出更低的腐蚀电流密度、广泛的钝化区域和卓越的再钝化能力。多元素固溶体的形成提高了高熵合金的化学稳定性,有利于形成更致密、均匀的氧化层,从而在多种腐蚀环境下提供有效的保护,使得高熵合金在能源、化工、海洋工程等领域具有广泛的应用潜力。
本文旨在综述高熵合金的腐蚀性能研究进展,揭示高熵合金的合金组成、微观结构和耐蚀性之间的关系,这些讨论将有利于耐腐蚀高熵合金的按需设计。最后,对未来高熵合金耐蚀性的研究提供了新的视角和思路。
合金成分是决定高熵合金性能的关键因素之一。不同元素的引入不仅可以改变高熵合金的晶体结构,影响其机械性能,还能显著改变高熵合金的电化学行为和耐蚀性。例如,某些元素如铬(Cr)、镍(Ni)和钼(Mo)被广泛认为是提高合金耐蚀性的重要元素,这是由于这些元素能在合金表面形成一层致密的钝化膜,有效阻隔腐蚀介质的侵蚀。通过实验数据和理论分析,按元素的化学稳定性及其对腐蚀环境中钝化膜形成和稳定性的作用进行讨论。
在不锈钢中,铬元素通过在表面形成一层致密的钝化膜来阻碍腐蚀的发生。这一钝化膜可以防止氧、水和其他腐蚀介质直接接触合金表面,从而减缓或阻止进一步的腐蚀反应。在高熵合金中,铬也可以发挥类似的作用,通过形成氧化物层防止合金表面腐蚀的发生。
Yan等[29]研究了Cr含量对Al0.3CrxFeCoNi(x=0~2.0原子百分比)高熵合金显微组织、腐蚀行为和力学性能的影响。图1[29]为Al0.3CrxFeCoNi合金的选区电子衍射图。
图1可以看到随着Cr含量的增加,合金结构由单一的面心立方(FCC)逐渐转变为含有体心立方(BCC)和有序B2相的复合结构。研究发现,在3.5% NaCl溶液(每100 g的NaCl水溶液中,含有3.5 g的NaCl固体和96.5 g的水)中,Al0.3CrxFeCoNi合金可以发生自发的钝化,随着合金中Cr含量的增加,合金的抗点蚀性能得到提高。当Cr含量为0~1.0时,合金容易发生点蚀,并且点蚀电位随合金中Cr含量的增加而升高。值得注意的是,在Cr含量为1.5~2.0时,合金在2V(vs. SCE)的阳极极化下没有出现点蚀现象。动电位极化后,Al0.3CrxFeCoNi(x=0~1.0原子百分比)合金表面形貌出现凹坑,而Al0.3CrxFeCoNi(x=1.5~2.0原子百分比)合金表面形貌无变化。结果表明,具有富Cr的FCC相和BCC/B2相的Al0.3CrxFeCoNi(x=1.5~2.0原子百分比)合金,由于表面形成了含有更多Cr2O3的钝化膜而具有优异的耐点蚀性能。有研究报道[13,30],双相FCC+BCC结构的AlxCrFeCoNi合金由于BCC相中Cr元素的缺乏而容易发生点蚀。相比之下,Al0.3CrxFeCoNi(x=1.5~2.0原子百分比)合金的BCC/B2相和FCC相中Cr元素的含量均达到30%以上,远远高于其他组成元素的含量,从而避免了由于BCC相中Cr元素缺乏而引起的点蚀。因此,提高Al−Cr−Fe−Co−Ni合金中的Cr含量是定制耐腐蚀合金的有效途径。
钼(Mo)是一种重要的合金元素,经常被添加到不锈钢和高熵合金中,以改善其性能,特别是在提高耐蚀性方面。Pan等[31]探讨了钼元素添加对(CoNiV)100−xMoxx=1,4原子百分比)合金在0.5 mol/L的H2SO4溶液中腐蚀行为的影响。通过电化学阻抗谱(EIS)、动态极化曲线和Mott−Schottky等测试,发现微量钼的添加显著提高了合金的耐蚀性。这种增强的耐蚀性主要归因于钼在钝化膜中的修复作用,其中钼倾向于在钝化膜的缺陷处富集,充当加强剂,有助于修复钝化膜并防止其进一步破裂。此外,钼的加入导致了合金微观结构的变化,包括晶格参数的增加和晶格畸变的加剧,这些微结构的变化进一步增强了合金的机械性能和耐蚀性。Wang等[32]研究了铸态AlCrFeNi3Moxx=0,0.1,0.2,0.3,0.4原子百分比)高熵合金在氯化物环境中的腐蚀性能。结果表明,含Mo元素的AlCrFeNi3Mox高熵合金耐蚀性能优于不含Mo元素的AlCrFeNi3高熵合金和316L不锈钢的耐蚀性。随着Mo含量的增加,特别是在AlCrFeNi3Mo0.3合金中,形成了富含Cr2O3和MoO3的致密钝化膜,如图2图3中的X射线光电子能谱(XPS)结果中所示[32]。这种钝化膜为合金表面提供了更好的保护,能有效阻挡氯离子的侵蚀,从而提高了合金的耐蚀性。这些研究结果验证了钼元素对提高合金耐腐蚀性的积极作用。
镍(Ni)作为一种重要的工业金属,因其出色的耐腐蚀性和机械性能而广泛应用于多种合金中,包括不锈钢和高熵合金[3335]。Ni的加入对高熵合金的相稳定性具有显著影响,它倾向于促进面心立方(FCC)相的形成,该相通常具有较好的延展性和韧性[36]
López Ríos等[35]研究了不同Ni元素含量对AlCrFeCoNixx = 1.0,1.4,1.8 原子百分比)高熵合金在生理液体中的微观结构、硬度以及耐腐蚀性能的影响,以评价这些合金作为生物医用材料的可能性。研究结果表明,这些合金在模拟生物环境中展现出较低的腐蚀速率和腐蚀电流密度,具有良好的稳定性。此外,在另一系列高熵合金Al2CrFeCoCuTiNixx=0,0.5,1.0,1.5,2.0原子百分比)的研究中[24],研究者评估了Ni元素的添加对高熵合金在质量百分比为3.5%的NaCl溶液(每100 g的NaCl水溶液中,含有3.5 g的NaCl固体和96.5 g的水)和1 mol/L NaOH溶液中耐腐蚀性能的影响。结果表明,在中性和碱性环境下,当Ni的摩尔比为1时,合金显示出最佳的耐蚀性。然而,当Ni的摩尔比>1时,腐蚀电流密度值随之增加,这表明存在一个镍含量的临界点,超过该点,镍的添加将对高熵合金的腐蚀性能产生负面影响。原因可能是在此研究中Ni元素原子半径相比于其他元素较小,当Ni含量较高时,合金的晶格畸变严重,影响合金的显微组织,从而影响合金的耐蚀性。
铝元素作为轻量化元素加入高熵合金后,有助于降低合金的密度,提高合金的比强度和比刚度,从而增强合金的力学性能。此外,铝元素也可以改变高熵合金的抗腐蚀性能。Zhao等[37]研究了铸态AlxCrFeNi3−xx=0.6,0.8,1.0原子百分比)高熵合金在氯离子环境中的腐蚀和钝化行为。如图4[37]的电化学测试结果所示,在质量百分比为3.5% 的NaCl溶液中,随着Al含量的增加,AlxCrFeNi3−x合金的耐蚀性逐渐降低。这可能是由于成分差异导致钝化膜的稳定性和保护能力下降所致。AlxCrFeNi3−x 3种合金均由FCC和B2结构组成,随着Al含量的增加,B2相的含量逐渐增加。如图5合金在动电位极化后的表面形貌所示,Al和Ni含量较高而Cr和Fe含量较低的B2相比FCC相更容易发生点蚀。
相反,Yan等[38]对AlxCoCuFeMn(x=0,0.25,0.5,0.75,1.0原子百分比)高熵合金在3.5% NaCl溶液中的腐蚀性能进行了研究,发现随着Al含量的增加,高熵合金的耐蚀性得到改善。具体而言,随着Al含量的增加,腐蚀电位增大,表明合金的抗腐蚀性提高,这被认为与高Al含量引起的晶格畸变导致的缺陷增多有关。而且含Al元素的AlCoCuFeMn高熵合金的腐蚀电流密度低于不含Al元素的CoCuFeMn高熵合金。此外,铝含量的增加还导致腐蚀产物的形成和点蚀的抑制。这些结果表明,铝含量的变化在高熵合金的耐蚀性中扮演着重要角色。
铌(Nb)元素对高熵合金耐蚀性的影响得到了深入的研究。Liao等[39]研究了Nb对Al0.3CrFeCoNiNbxx=0.25,0.35,0.4,0.45,0.5,0.75原子百分比)高熵合金的微观结构、腐蚀行为和机械性能的影响。该团队发现,随着铌含量从x=0.25增加到x=0.75,合金的微观结构经历了从亚共晶(主要为FCC相)到共晶(FCC+Laves),再到过共晶(主要为Laves相)的转变。这些变化对合金的性能有显著影响:在质量百分比为3.5%的NaCl溶液中,添加铌显著提高了合金的耐蚀性,这主要归因于铌和铬氧化物富集的保护性钝化膜的形成。这种钝化膜在化学上稳定,有效提升了合金的耐蚀性,尤其是当铌含量为0.45时,钝化膜中的铌氧化物分布更为均匀,阻抗值也更高(表1),从而展现出最优的耐蚀性。图6[39]为合金动极化后的表面形貌,可以看到当铌含量为0.45时,蚀坑尺寸最小。此外,还发现随着Nb含量的增加,合金的强度和硬度得到了提高,但是由于脆性Laves相的增多,塑性变形能力显著降低。此外,Liu等[40]进一步深入探讨了FeCoCrNiNbxx=0,0.15,0.33,0.5原子百分比)共晶高熵合金在H2SO4溶液中的腐蚀与钝化行为。研究发现,这些合金在0.5 mol/L的H2SO4溶液中形成了双层结构钝化膜,内层钝化膜较为致密且具有更好的保护性。高的Cr/Nb比例有助于FeCoCrNiNbx合金的钝化及较厚钝化膜的形成,从而显著增强合金表面的保护能力。这些研究成果不仅揭示了铌在调控高熵合金性能中的关键作用,还为设计具有优异耐蚀性和机械性能的新型合金提供了重要的理论依据。
一些商用双相不锈钢含有质量百分比为0.5%~1.0%的铜,通过降低在非氧化环境,特别是硫酸中的腐蚀速率来提高耐腐蚀性[4142]。Cu在高熵合金中的作用比较复杂,迄今为止,有大量研究探讨了Cu添加对高熵合金腐蚀行为的影响及腐蚀机理[4345]。Wang等[46]采用真空电弧熔炼过程制备了不同Cu含量的AlCrFeNi3Cuxx=0,0.2,0.4,0.6,0.8,1.0原子百分比)高熵合金。通过在质量百分比为3.5%的NaCl溶液中进行静态浸泡测试以及通过动电位极化测试、EIS、Mott−Schottky分析和XPS对钝化膜进行分析,详细评估了这些合金的耐腐蚀性。研究发现,Cu的添加改变了高熵合金的微观结构,从FCC+B2双相共晶结构转变为树枝状结构。特别是AlCrFeNi3Cu0.4合金,在所有测试的合金中显示出了最佳的耐腐蚀性,其腐蚀速率显著低于其他Cu含量的合金及316L不锈钢。
对比分析AlCrFeNi3Cu0和AlCrFeNi3Cu0.4高熵合金钝化膜形成的Mott−Schottky曲线,发现Cu的添加能够降低钝化膜中的缺陷密度,从而提高钝化膜的稳定性。此外,XPS分析揭示了钝化膜中的元素分布,特别是Cr2O3、Al2O3和CuO在提高钝化膜保护性中的关键作用。通常,Cr的氧化物倾向于形成致密的钝化膜,而Al、Cu的氧化物倾向于形成多孔的钝化膜[37, 47]。与AlCrFeNi3(不含Cu)合金比,AlCrFeNi3Cu0.4合金钝化膜中Cr2O3和Cr(OH)3的比例增加,表明AlCrFeNi3Cu0.4合金钝化膜在NaCl溶液中具有更强的保护作用。图7[46]为AlCrFeNi3Cux高熵合金在质量百分比为3.5%的NaCl溶液中腐蚀过程,不含Cu显示出B2相的选择性腐蚀,这可能是由于电偶腐蚀,即B2相与FCC相电位的差异引起的。而Cu0.2和Cu0.4合金中,富Cr的BCC沉淀颗粒在枝晶间形成了致密的Cr2O3钝化膜,有效降低了这些合金发生局部腐蚀的可能性。同时,这些合金的钝化膜还富集了Al2O3和CuO,尤其是在枝晶间区,增强了腐蚀保护。然而,Cu元素的过量加入使合金的晶粒尺寸变细,从而扩大了晶界,加速了点蚀的发生。此外,由于铜的抗菌作用,含Cu高熵合金[48]在微生物影响的腐蚀中发挥了重要作用。
在高熵合金中,锡(Sn)作为一种重要的合金元素,扮演着重要的角色。添加锡不仅可以改善高熵合金的力学性能,还能显著提升其抗腐蚀性能[4950]。Chang等[49]的研究发现,Sn的添加对CoCrFeNiSnxx=0,0.1,0.5,1.0原子百分比)高熵合金在氯离子环境中的腐蚀行为产生了显著影响。图8为循环动电位极化测试至腐蚀电流密度为5 mA/cm2后4种CoCrFeNiSnx高熵合金的表面形貌,当Sn的添加量较低(x=0.1原子百分比)时,由于FCC相和新形成的Ni3Sn2相之间存在显著的电位差,这种电偶效应促进了点蚀的发生,从而加剧了合金的腐蚀。然而,随着Sn含量进一步增加到x=0.5和x=1.0时,观察到合金的腐蚀抵抗性显著提高,极化后腐蚀坑逐渐消失。这种改善主要归因于Ni3Sn2相的比例增加,它作为一个较大的阴极区域存在,有效地降低了FCC相的点蚀敏感性,并且增强了合金在氯离子环境中的整体稳定性。此外,Sn的添加还促使合金微观结构细化,如图9[49]所示,随着Sn含量的增加,合金呈现典型的枝晶形貌。这种结构的细化有助于均匀分布腐蚀电流,进一步降低局部腐蚀的可能性。这些结果表明,通过调整Sn的添加比例,可以显著改善高熵合金的耐蚀性,为设计和开发新型耐腐蚀材料提供了重要的实验依据和理论指导。
除了上面列出的合金元素外,高熵合金中还有许多其他可选元素,如Ti、Cu、Si、Mn、Co、C、W、Nb、Hf、Ta、Zr等。在大多数水溶液中,Ti表现出较低的临界钝化电位和较宽的钝化区域,这表明其具有优异的耐腐蚀性。Qi等[51]研究了Al和Ti对CoCrFeNi(Al0.3Ti0.2xx=0.25,0.5,0.75,1.0原子百分比)高熵合金耐蚀性能的影响。在质量百分比为3.5%的NaCl溶液中,Al和Ti含量的增加提高了钝化膜的稳定性,同时有效降低了合金的腐蚀电流密度和钝化电流密度。而大量强化相的析出加剧了元素的偏析,导致合金的抗点蚀性能下降。Si的加入在高熵合金中容易与Al、Fe、Ni、Mn、Cr等形成金属间相,如Cr3Si和CrFe8Si,这些金属间相增加了钝化膜的非均匀性,降低了AlCoCrFeNiSixx=0,0.1,0.2,0.3,0.4原子百分比)合金在质量百分比为3.5%的NaCl溶液中的耐蚀性[52]。因此,探索合金元素间的相互作用及其对腐蚀行为的综合影响,可以为设计和开发新型、具有优异耐蚀性的高熵合金材料提供宝贵的指导。
除了成分设计,各种热处理工艺,如正火、退火、淬火、回火、轧制和时效等,均可改变高熵合金的微观结构,包括晶粒细化、位错密度增加,以及相分布调整等,进而对其耐蚀性能产生显著影响。铸态高熵合金往往伴随柱状晶或粗大等轴晶的形成,且在凝固过程中容易产生成分偏析与晶界元素富集,这种微观不均匀性在腐蚀介质中易形成腐蚀微电偶,从而加剧晶间腐蚀[53]。而热轧或冷轧等变形加工可细化晶粒、打碎铸态组织,提高组织均匀性,增强抗腐蚀性[5455]。其次,热处理工艺在调控析出相与固溶体状态方面发挥关键作用[5657]。某些高熵合金体系(如CoCrFeNiMo、AlCoCrFeNi等)在退火或时效处理后会发生第二相析出或σ相等脆性相的形成,这些析出相往往沿晶界分布,成为腐蚀优先发生成分析出区,进而显著降低合金耐蚀性[5859]。Zhang等[60]探讨了重熔和退火处理对AlFeNiCoCuCr高熵合金在质量百分比为3.5%的NaCl溶液中耐蚀性的影响。研究发现,与铸态相比,经过退火和重熔处理的合金展示了更优异的耐蚀性,其中,退火处理的合金耐蚀性最佳,合金的腐蚀类型由点蚀和晶间腐蚀转变为单点蚀。原因是经过重熔和退火处理后,随着晶核生长的不同,枝晶区域增加,枝晶间Cu偏析区域减少,AlFeNiCoCuCr高熵合金的耐蚀性在一定程度上得到了提高。
在各种微观结构参数中,晶粒尺寸对金属的腐蚀行为起着重要作用,这一点被广泛认知[1617]。在高熵合金以及传统金属中,关于晶粒尺寸对其腐蚀行为的影响存在矛盾的发现。一些研究人员已经报道了小晶粒尺寸对高熵合金耐腐蚀性能的积极影响[6163]。Xue等[61]对单相Al2Cr5Cu5Fe53Ni35高熵合金进行冷轧退火处理,研究其在模拟海水溶液中的腐蚀行为。发现随着晶粒尺寸的增大,合金的耐蚀性降低。Bahmani等[62]研究了非等原子Fe41Mn25Ni24Co8Cr2高熵合金通过高比差速轧制后,退火处理对其耐蚀性的影响。研究结果显示,在3.5~84.9 µm的晶粒尺寸范围内,随着晶粒尺寸的减小,合金的耐蚀性提高。在图10(a)~(d)电化学测试曲线中可以看出,A800和A900样品的耐腐蚀性能比其他样品好,随着退火温度的提高,样品的耐腐蚀性能有所下降。图10(e)说明了在小晶粒和大晶粒退火样品中观察到的腐蚀机理。首先,通过优先晶向点蚀(PCP)机制引起晶体取向的变化,这种取向的变化对于阻止腐蚀坑的形成和扩展至关重要[64]。其次,晶粒尺寸减小导致晶界密度增加,这种增加的晶界密度为铬离子向合金表面的运输提供了具有更高扩散性的路径。较小的晶粒尺寸提供了更多的铬氧化物的成核位点,这有助于在合金表面形成更加均匀致密的钝化膜。钝化膜的均匀致密性是提高耐蚀性的关键因素之一。尽管A700样品具有最小的晶粒尺寸,但与A800和A900样品相比,在耐腐蚀性方面没有明显的优势。这是因为在700℃退火后,晶界附近存在高密度的位错和非平衡晶界,导致氧化铬层上形成坑和裂纹。
然而,小晶粒尺寸对高熵合金腐蚀性能的负面影响也有报道[1617, 65]。Han等[16]采用低温轧制后短时间退火制备了超细晶(UFG)等原子CoCrFeMnNi高熵合金,发现UFG高熵合金更容易受到电偶腐蚀和点蚀。这主要归因于UFG合金表面更容易形成不稳定的钝化层。通过冷轧和短时间退火处理的UFG合金,在晶粒内部和晶界中离散地分布着一些富Cr贫Ni区域,如图11(a)~(b)所示,称为σ相[66],这将导致试样表面的基体相和σ相之间发生电偶腐蚀,加速了UFG合金在腐蚀初期的溶解速率。而在图11(c)~(d)粗晶(CG)合金中,元素分布均匀,避免了电偶腐蚀的发生。此外,UFG样本的钝化膜中铬元素含量减少,钝化膜中的铬缺失导致其稳定性降低,进而降低了合金对点蚀的抵抗力。
综上所述,晶粒尺寸对高熵合金的腐蚀速率的影响是一个复杂的现象,它受到多种因素的影响,包括晶界密度、合金的化学组成、腐蚀介质的性质以及钝化膜的稳定性等。因此,深入理解这些影响因素及其相互作用对于优化合金的耐蚀性能至关重要。
尽管对高熵合金在腐蚀行为方面的研究已取得显著进展,但这一领域仍存在许多需要深入研究的问题,如环境因素的影响、合金成分的优化等。未来的研究不仅可以深化对现有高熵合金腐蚀机理的理解,还能推动新型合金设计的发展,以满足工业应用中对高性能耐蚀材料的需求。在此背景下,提出以下3点建议,希望能为高熵合金的腐蚀研究提供新的视角和动力。
为了充分理解高熵合金在多样腐蚀环境下的耐腐蚀性能,必须进行更为深入的实验与理论研究,特别是针对不同高熵合金体系中局部腐蚀行为的机制探索,如点蚀和缝隙腐蚀等。这些局部腐蚀行为的发生与合金的具体相组成、微观结构、表面特性及其在特定腐蚀环境下的应用电位紧密相关。同时,腐蚀介质的离子浓度、温度、pH值等参数也在很大程度上影响着钝化膜的质量,包括其结构、厚度、电子特性及化学组成等,而这些特性又直接关联到高熵合金的耐腐蚀能力。为了全面表征和理解钝化膜的复杂性,需要先进的表面分析技术,如透射电镜(TEM)、原子力显微镜(AFM)、飞行时间二次离子质谱(ToF−SIMS)、XPS和俄歇电子能谱(AES),以及原位表征技术。通过结合高分辨率的表征技术和原位监测手段,深入探讨高熵合金的腐蚀机理,将为开发具有优异耐腐蚀性的新型高熵合金材料奠定坚实的基础。
随着材料科学领域对数据密集型研究方法的接受度日益提高,机器学习(ML)在预测材料性能、加速材料发现和设计等方面已展现出显著优势[6769]。例如,Roy等[70]利用生成对抗网络与神经网络模型相结合,通过过滤具有高硬度的成分组合来设计多主元素合金。即使在高维特征空间中,ML模型也能够生成优化的成分,其中一个成分的硬度(941 HV)比训练数据中的最大值(857 HV)高10%。机器学习算法可以通过分析大量现有合金及其组成和耐蚀行为的数据集来预测具有优越耐蚀性的新型高熵合金组成。这种方法可以识别出那些依靠传统冶金原理的研究人员可能无法直观发现的新型合金体系,从而可以大大减少实验测试的时间和成本[7172]。此外,利用先进的计算材料科学方法,如第一性原理计算、相场模拟等,对高熵合金的腐蚀过程进行多尺度建模和模拟,以预测合金的腐蚀行为和寿命。未来的研究应利用这些工具加速新型高熵合金的发现和优化。
高熵合金的广泛应用需要综合考虑其力学性能、加工性、成本效益和耐腐蚀性。因此,未来的研究应建立包括强度、韧性、耐腐蚀性、抗菌性等在内的综合性能评估体系。尤其是考虑到工业应用中对成本效益的要求,开发低成本合金的同时应优化其生产工艺,提高生产效率,以确保高熵合金在大规模工业应用中的可行性。同时,在材料的可持续性方面,研究人员应关注高熵合金的回收利用问题,开发可循环使用的合金体系,提升其在极端环境下的长期稳定性和性能。
合金元素的变化或原子比的调整以及不同的热机械加工条件会深刻影响高熵合金的微观结构,从而在改变高熵合金腐蚀行为的同时,使保护钝化膜具有不同的特性。特别是,某些合金元素如Cr、Mo和Ni能够在合金表面形成稳定的钝化膜,显著提高合金的耐蚀性。此外,加工工艺如退火、轧制等能够细化晶粒、调整相结构,进而影响合金的腐蚀行为。材料的腐蚀性能限制了工业产品的实际使用寿命和安全性,高熵合金的腐蚀研究需要更深入的实验和理论工作,以揭示复杂腐蚀过程中的微观机制,并探索新的合金设计策略。
  • 国家自然科学基金项目(52361145849, 52371047)
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2025年第43卷第17期
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doi: 10.3981/j.issn.1000-7857.2024.12.01749
  • 接收时间:2024-12-16
  • 首发时间:2025-12-18
  • 出版时间:2025-09-13
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  • 收稿日期:2024-12-16
  • 修回日期:2025-08-01
  • 录用日期:2025-09-01
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国家自然科学基金项目(52361145849, 52371047)
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    北京科技大学新材料技术研究院,北京 100083

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骆鸿(通信作者),教授,研究方向为先进耐蚀材料的设计和服役安全,电子信箱:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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