Elastic-viscoplastic constitutive model for capturing the mechanical response of polymer composite at various strain rates

Ali, Shahzad Fateh; Fan, Jihui

doi:10.1016/j.jmst.2020.05.013

Cited by 24 publications

(4 citation statements)

References 34 publications

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“…As expected for an elastomeric material, the properties of the SFIE depended on the stretching rate. − The yield stress increased with increasing strain rate (Figure D), and the toughness increased linearly from 115 to 614 MJ m –3 (inset plot in Figure D) as stretching rate increased from 10 to 400 mm min –1 . In situ birefringent observations (Figure E) showed that the response of the welding-entanglement network was reversible.…”

Section: Resultssupporting

confidence: 56%

Dual Physically Crosslinked Silk Fibroin Ionoelastomer with Ultrahigh Stretchability and Low Hysteresis

Zhang

Cao

et al. 2023

Chem. Mater.

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Innovations in ionotronics have greatly facilitated the development of ultraflexible devices, displays, and machines. However, ionotronics often suffer from an inherent trade-off between stretchability and elasticity due to their viscous nature. Here, we overcome this limitation by incorporating dimension-and-content-confined nanocrystalline crosslinks into amorphous and molecularly entangled silk fibroin (SF) ionotronics. The welding-entanglement network exhibited a 34-fold increase in Young’s modulus, a 14-fold increase in tensile strength, and a 9-fold increase in toughness compared to the same SF material without the nanocrystal crosslinks. The welding-entanglement network also gained recoverable hysteretic energy dissipation with hysteresis as low as 22%. The stretchability and low hysteresis exhibited by these SF ionoelastomers far exceed those of previously reported ionotronics and bioelastomers. These mechanical merits, together with the inherent advantages of SF ionoelastomers, including their multiprocessability, sustainability, biocompatibility, and degradability, promote their applications in human–machine interfaces and bio-functional devices.

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Section: Resultssupporting

confidence: 56%

Dual Physically Crosslinked Silk Fibroin Ionoelastomer with Ultrahigh Stretchability and Low Hysteresis

Zhang

Cao

et al. 2023

Chem. Mater.

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“…About the yield point, there are two different arguments in the literature. First, the deformation was considered to be accomplished by inter‐lamellar and intra‐lamellar slips 36–40 ; second, stress‐induced melting and recrystallization were proposed to be responsible for the deformation process 41,42 . Further investigations revealed that both processes discussed above may be activated at different strains during tensile deformation 43 .…”

Section: Resultsmentioning

confidence: 99%

Physico‐mechanical properties of poly(ethylene glycol)‐based polymer networks

Bilal,

Mahmood,

Samiullah

et al. 2023

J of Applied Polymer Sci

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Poly(ethylene glycol) (PEG) based networks have been used extensively for biomedical applications and as solid state electrolytes. In this work, a series of PEG networks was prepared using bifunctional PEG of molar mass 400, 1000, 2000, 4000, and 6000 g mol−1 and star shaped PEG (Mn = 1000 g mol−1) cross‐linker using copper‐catalyzed Huisgen 1,3‐dipolar cycloaddition or “click” chemistry. The end‐group modification of the bifunctional polymers and the star shaped cross‐linker with alkyne and azide groups, respectively, was confirmed by 1H NMR spectroscopy. The coupling reaction between azide and alkyne functionalities for the network formation was confirmed by Fourier transform infrared (FTIR) spectroscopy. The thermal properties of polymer network were determined by differential scanning calorimetry. Swelling studies of the networks were performed to correlate the network structure with the physical properties. The molar mass between the cross‐links was determined using the Bray‐Merill modified Flory‐Rehner equation. The effect of molar mass between the cross‐linking points on the strength of the networks was compared. Additionally, the effect of the stoichiometry of the precursors on the network strength was also studied. Finally, thickness‐dependent tensile testing was performed to investigate the stress oscillation phenomenon.

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“…When the glass transition temperature is lower than room temperature, the polymers are rigid, like polymethyl methacrylate. They also can be applied for energy absorption after microstructural modifications [ 67 , 68 , 69 , 70 , 71 ]. Therefore, this work offers an intellectual method for determining the glass transition temperature of polymers for applications as absorbent materials of mechanical energy.…”

Section: Discussionmentioning

confidence: 99%

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

Uddin,

Fan

2024

Polymers

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The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model’s output. This framework can be adaptable to other properties at a low computational expense.

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Elastic-viscoplastic constitutive model for capturing the mechanical response of polymer composite at various strain rates

Cited by 24 publications

References 34 publications

Dual Physically Crosslinked Silk Fibroin Ionoelastomer with Ultrahigh Stretchability and Low Hysteresis

Dual Physically Crosslinked Silk Fibroin Ionoelastomer with Ultrahigh Stretchability and Low Hysteresis

Physico‐mechanical properties of poly(ethylene glycol)‐based polymer networks

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

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