A visco-hyperelastic model for hydrogels with tunable water content

Zhong, Danming; Xiang, Yiqiang; Wang, Zhicheng; Chen, Zhe; Liu, Junjie; Wu, Zi Liang; Qu, Shaoxing; Yang, Wei

doi:10.1016/j.jmps.2023.105206

Cited by 35 publications

(9 citation statements)

References 69 publications

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“…The morphology of the PVA/MWCNTs hydrogel was observed by a scanning electron microscopy (SEM, Quanta FEG 250, American FEI Company) at an accelerating voltage of 20 kV (the samples were freeze‐dried before testing). Apparent density ( ρ ) and water content ( Φ ) can be obtained by simple measurement and formula (1–2) 37,38 :

Φ = \frac{Ws - Wd}{italicWs} \times 100 %,

where Ws was the weight of the original hydrogel sample, and Wd was the weight of the dried hydrogel sample after 24 h of drying at 70°C in a vacuum drying chamber.

ρ = \frac{W}{π \times {((), \frac{D}{2})}^{2} \times H},

where W is the weight of the hydrogel, D is the diameter, and H is the thickness of the hydrogel.…”

Section: Methodsmentioning

confidence: 99%

A novel PVA/MWCNTs hydrogel with high toughness and electromagnetic shielding properties

Xu,

Tan,

Xue

et al. 2023

Polymer Engineering & Sci

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Carbon nanotubes (CNTs) are widely utilized as conductive fillers due to their high conductivity, lightweight, and ease of modification. However, their poor dispersion limits their otherwise excellent performance. In this study, we successfully prepared a high toughness, strong electromagnetic interference (EMI) shielding hydrogel by blending polyvinyl alcohol (PVA) hydrogel with multi‐walled carbon nanotubes (MWCNTs) modified by anionic surfactant sodium dodecylbenzene sulfonate (SDBS) and (3‐aminopropyl) triethoxysilane (KH550). We found that the modifiers can not only promote the uniform dispersion of MWCNTs but also improve the mechanical properties of the hydrogel. The results demonstrate that the stress (≈14.86 MPA), elongation at break (233.9%), and compressive strength (≈2.89 MPA) of PVA/MWCNTs composite hydrogel with 1 wt% MWCNTs and 5 wt% PVA were increased by 533.6%, 241.4%, and 206.5%, respectively. Encouragingly, the total electromagnetic shielding effectiveness (SET) is 123.6 dB at a thickness of 5 mm. In summary, this work provides a general approach to functionalizing MWCNTs, which has great potential for developing EMI shielding materials with high toughness.

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Φ = \frac{Ws - Wd}{italicWs} \times 100 %,

where Ws was the weight of the original hydrogel sample, and Wd was the weight of the dried hydrogel sample after 24 h of drying at 70°C in a vacuum drying chamber.

ρ = \frac{W}{π \times {((), \frac{D}{2})}^{2} \times H},

where W is the weight of the hydrogel, D is the diameter, and H is the thickness of the hydrogel.…”

Section: Methodsmentioning

confidence: 99%

A novel PVA/MWCNTs hydrogel with high toughness and electromagnetic shielding properties

Xu,

Tan,

Xue

et al. 2023

Polymer Engineering & Sci

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“…[9] Accurate fracture prediction can lead to the development of more robust hydrogels, minimizing the risk of premature failure and reducing DOI: 10.1002/adts.202300776 the need for frequent replacement or maintenance. Traditionally, the study of hydrogel and hyperelastic material fracture behavior has been conducted through experimental methods, [10][11][12][13] numerical simulations, [14][15][16][17] and the development of theoretical models. [18][19][20][21] Jia et al [11] established a quantitative framework for decomposing the fracture toughness and feature sizes of the crack-tip field, which provides important insights into the underlying toughening mechanism of DN gels.…”

Section: Introductionmentioning

confidence: 99%

“…[ 9 ] Accurate fracture prediction can lead to the development of more robust hydrogels, minimizing the risk of premature failure and reducing the need for frequent replacement or maintenance. Traditionally, the study of hydrogel and hyperelastic material fracture behavior has been conducted through experimental methods, [ 10–13 ] numerical simulations, [ 14–17 ] and the development of theoretical models. [ 18–21 ] Jia et al.…”

Section: Introductionmentioning

confidence: 99%

Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network

Zheng,

You,

Lam

et al. 2024

Advcd Theory and Sims

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Hydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short‐term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems.

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“…[19][20][21][22] However, the majority of these tough hydrogels usually show typical strainsoftening behavior due to the force-induced dissociation of sacrificial bonds at large strain, 23,24 which is contrary to the general mechanical behavior of natural skin. Besides, the dispersed solvent inside the hydrogel, i.e., the water, is of intrinsic low conductivity itself and is highly volatile in dry air, 25,26 which significantly impedes the practical application of a wearable sensor during daily usage. Recently, ionogels composed of a polymeric backbone and dispersed ionic liquids (ILs) have attracted great attention due to the characteristics of ionic conduction, non-volatility and non-freezability.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the dispersed solvent inside the hydrogel, i.e. , the water, is of intrinsic low conductivity itself and is highly volatile in dry air, 25,26 which significantly impedes the practical application of a wearable sensor during daily usage. Recently, ionogels composed of a polymeric backbone and dispersed ionic liquids (ILs) have attracted great attention due to the characteristics of ionic conduction, non-volatility and non-freezability.…”

Section: Introductionmentioning

confidence: 99%