Microstructure-environment related fatigue deformation-cracking behavior and data-physics driven crack growth life prediction of perfluorosulfonic acid ionomer

Yuzhe Jin; Wei Li; Serjouei Ahmad; Xiaobo Cao; Zifan Hu; Liang Cai; Pilin Song

doi:10.1016/j.tafmec.2025.105418

Microstructure-environment related fatigue deformation-cracking behavior and data-physics driven crack growth life prediction of perfluorosulfonic acid ionomer

Yuzhe Jin
, Wei Li^*
, Serjouei Ahmad
, Xiaobo Cao
, Zifan Hu
, Liang Cai
, Pilin Song

^*此作品的通讯作者

Beijing Institute of Technology
Nottingham Trent University

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

Perfluorosulfonic acid (PFSA) ionomers often suffer fatigue failure under complex environmental conditions, limiting their long-term reliability. However, the effects and underlying mechanisms of environmental factors such as hygrothermal conditions and corrosion on their multi-scale constitutive response and fatigue crack growth (FCG) behavior remain poorly understood. To address this gap, we integrate experimental insights with a physics-informed machine learning (PIML) framework for fatigue life prediction. In-situ experiments and microstructural characterization were performed to quantify the effects of temperature, humidity, and corrosive environments on the viscoelastic response and fatigue crack growth of PFSA ionomer. Results reveal that under ambient conditions, PFSA exhibits typical viscoelastic behavior, explained by a proposed three-level hierarchical structural model. Hygrothermal exposure leads to significant thermal softening and network disruption through hydrogen bonding, and water cluster interactions, whearas chemical corrosion induces side-chain damage and subsequent macroscopic structural destruction. These environmental degradations markedly accelerate FCG rates and alter fracture morphologies. Leveraging these insights, a dual-network Physics-Informed Machine Learning model (PIML-PR) was developed to predict FCG behavior under coupled environmental and mechanical conditions. By fusing physically derived parameters with environmental descriptors, the model achieves high predictive accuracy (≈98 %) in capturing environment-sensitive FCG behavior. This integrated experimental–computational framework provides a pathway for physics-informed lifetime assessment of polymer electrolyte membranes under service-relevant conditions.

源语言	英语
期刊论文编号	105418
期刊	Theoretical and Applied Fracture Mechanics
卷	142
DOI	https://doi.org/10.1016/j.tafmec.2025.105418
出版状态	已出版 - 3月 2026
已对外发布	是

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@article{b0aa55310e214df8a166b7a47e619f8b,

title = "Microstructure-environment related fatigue deformation-cracking behavior and data-physics driven crack growth life prediction of perfluorosulfonic acid ionomer",

abstract = "Perfluorosulfonic acid (PFSA) ionomers often suffer fatigue failure under complex environmental conditions, limiting their long-term reliability. However, the effects and underlying mechanisms of environmental factors such as hygrothermal conditions and corrosion on their multi-scale constitutive response and fatigue crack growth (FCG) behavior remain poorly understood. To address this gap, we integrate experimental insights with a physics-informed machine learning (PIML) framework for fatigue life prediction. In-situ experiments and microstructural characterization were performed to quantify the effects of temperature, humidity, and corrosive environments on the viscoelastic response and fatigue crack growth of PFSA ionomer. Results reveal that under ambient conditions, PFSA exhibits typical viscoelastic behavior, explained by a proposed three-level hierarchical structural model. Hygrothermal exposure leads to significant thermal softening and network disruption through hydrogen bonding, and water cluster interactions, whearas chemical corrosion induces side-chain damage and subsequent macroscopic structural destruction. These environmental degradations markedly accelerate FCG rates and alter fracture morphologies. Leveraging these insights, a dual-network Physics-Informed Machine Learning model (PIML-PR) was developed to predict FCG behavior under coupled environmental and mechanical conditions. By fusing physically derived parameters with environmental descriptors, the model achieves high predictive accuracy (≈98 \%) in capturing environment-sensitive FCG behavior. This integrated experimental{\textendash}computational framework provides a pathway for physics-informed lifetime assessment of polymer electrolyte membranes under service-relevant conditions.",

keywords = "Chemical corrosion, Hygrothermal condition, Microstructural mechanism, Perfluorosulfonic acid ionomer, Physics-informed machine learning",

author = "Yuzhe Jin and Wei Li and Serjouei Ahmad and Xiaobo Cao and Zifan Hu and Liang Cai and Pilin Song",

note = "Publisher Copyright: {\textcopyright} 2025 Elsevier Ltd",

year = "2026",

month = mar,

doi = "10.1016/j.tafmec.2025.105418",

language = "English",

volume = "142",

journal = "Theoretical and Applied Fracture Mechanics",

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publisher = "Elsevier B.V.",

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T1 - Microstructure-environment related fatigue deformation-cracking behavior and data-physics driven crack growth life prediction of perfluorosulfonic acid ionomer

AU - Jin, Yuzhe

AU - Li, Wei

AU - Ahmad, Serjouei

AU - Cao, Xiaobo

AU - Hu, Zifan

AU - Cai, Liang

AU - Song, Pilin

PY - 2026/3

Y1 - 2026/3

N2 - Perfluorosulfonic acid (PFSA) ionomers often suffer fatigue failure under complex environmental conditions, limiting their long-term reliability. However, the effects and underlying mechanisms of environmental factors such as hygrothermal conditions and corrosion on their multi-scale constitutive response and fatigue crack growth (FCG) behavior remain poorly understood. To address this gap, we integrate experimental insights with a physics-informed machine learning (PIML) framework for fatigue life prediction. In-situ experiments and microstructural characterization were performed to quantify the effects of temperature, humidity, and corrosive environments on the viscoelastic response and fatigue crack growth of PFSA ionomer. Results reveal that under ambient conditions, PFSA exhibits typical viscoelastic behavior, explained by a proposed three-level hierarchical structural model. Hygrothermal exposure leads to significant thermal softening and network disruption through hydrogen bonding, and water cluster interactions, whearas chemical corrosion induces side-chain damage and subsequent macroscopic structural destruction. These environmental degradations markedly accelerate FCG rates and alter fracture morphologies. Leveraging these insights, a dual-network Physics-Informed Machine Learning model (PIML-PR) was developed to predict FCG behavior under coupled environmental and mechanical conditions. By fusing physically derived parameters with environmental descriptors, the model achieves high predictive accuracy (≈98 %) in capturing environment-sensitive FCG behavior. This integrated experimental–computational framework provides a pathway for physics-informed lifetime assessment of polymer electrolyte membranes under service-relevant conditions.

AB - Perfluorosulfonic acid (PFSA) ionomers often suffer fatigue failure under complex environmental conditions, limiting their long-term reliability. However, the effects and underlying mechanisms of environmental factors such as hygrothermal conditions and corrosion on their multi-scale constitutive response and fatigue crack growth (FCG) behavior remain poorly understood. To address this gap, we integrate experimental insights with a physics-informed machine learning (PIML) framework for fatigue life prediction. In-situ experiments and microstructural characterization were performed to quantify the effects of temperature, humidity, and corrosive environments on the viscoelastic response and fatigue crack growth of PFSA ionomer. Results reveal that under ambient conditions, PFSA exhibits typical viscoelastic behavior, explained by a proposed three-level hierarchical structural model. Hygrothermal exposure leads to significant thermal softening and network disruption through hydrogen bonding, and water cluster interactions, whearas chemical corrosion induces side-chain damage and subsequent macroscopic structural destruction. These environmental degradations markedly accelerate FCG rates and alter fracture morphologies. Leveraging these insights, a dual-network Physics-Informed Machine Learning model (PIML-PR) was developed to predict FCG behavior under coupled environmental and mechanical conditions. By fusing physically derived parameters with environmental descriptors, the model achieves high predictive accuracy (≈98 %) in capturing environment-sensitive FCG behavior. This integrated experimental–computational framework provides a pathway for physics-informed lifetime assessment of polymer electrolyte membranes under service-relevant conditions.

KW - Chemical corrosion

KW - Hygrothermal condition

KW - Microstructural mechanism

KW - Perfluorosulfonic acid ionomer

KW - Physics-informed machine learning

UR - https://www.scopus.com/pages/publications/105025191520

U2 - 10.1016/j.tafmec.2025.105418

DO - 10.1016/j.tafmec.2025.105418

M3 - Article

AN - SCOPUS:105025191520

SN - 0167-8442

VL - 142

JO - Theoretical and Applied Fracture Mechanics

JF - Theoretical and Applied Fracture Mechanics

M1 - 105418

ER -

Microstructure-environment related fatigue deformation-cracking behavior and data-physics driven crack growth life prediction of perfluorosulfonic acid ionomer

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