Application of Hyperelastic Models in Mechanical Properties prediction of Mouse Oocyte and Embryo Cells at Large Deformations

Abbasi, Ali; Ahmadian, M.T.; Alizadeh, Ali Mohammad; Tarighi, S.

doi:10.24200/sci.2017.4321

Cited by 5 publications

(3 citation statements)

References 31 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is simple to use and can make good approximations at relatively small strains. The Neo-Hookean strain energy potential form is as follows [17]:…”

Section: Fig 15 Geometry Boundary Conditions and Loading For Fe Analy...mentioning

confidence: 99%

Comparison of energy absorption in negative stiffness honeycombs made of Polyamide 11 and 12

Shafipour,

Brooghani

2024

Preprint

View full text Add to dashboard Cite

Negative stiffness honeycombs are energy absorbers that absorb energy by transitioning from one buckling mode to another and their snap-through behavior in cells. They have a fundamental property as an energy absorber; in this type of absorber, negative stiffness causes reversibility after impact. In this study, the effect of negative stiffness honeycomb material in increasing energy absorption is investigated experimentally and numerically. The quasi-static tests are performed on negative stiffness honeycombs made of polyamide 11 and 12. Then, the finite element (FE) model of the structure is simulated under quasi-static compression. The FE model requires the nonlinear elastic and viscoelastic properties of the materials used to make the honeycombs. For this purpose, tensile and stress relaxation tests are performed on standard specimens. The Prony series coefficients for two polyamide materials 11 and 12 are extracted and used in the FE model. Next, the energy absorption performance of negative stiffness honeycombs of polyamide 11 and 12 is compared experimentally and numerically. The comparison shows that the energy absorption per unit mass (SEA) for a negative stiffness honeycomb made of polyamide 11 is 1.7 times higher in the experimental data and 1.62 times higher in the FE result than the negative stiffness honeycomb made of Polyamide12. Other fundamental parameters of energy absorption also confirm the higher efficiency of negative stiffness honeycombs made of polyamide 11.

show abstract

“…It is simple to use and can make good approximations at relatively small strains. The Neo-Hookean strain energy potential form is as follows [17]:…”

Section: Fig 15 Geometry Boundary Conditions and Loading For Fe Analy...mentioning

confidence: 99%

Comparison of energy absorption in negative stiffness honeycombs made of Polyamide 11 and 12

Shafipour,

Brooghani

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Levenberg-Marquardt algorithm was used to facilitate the non-linear least squares curve fitting process 24 . This solver was frequently used in similar studies involving hyperelastic materials, owing to its ease of use in the context of most finite element analysis software [25][26][27] .…”

Section: Selection Of Materials Modelsmentioning

confidence: 99%

Comprehensive constitutive modeling and analysis of multi-elastic polydimethylsiloxane (PDMS) for wearable device simulations

Zulkifli,

Moon,

Hyun

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Within the field of wearable devices, polydimethylsiloxane (PDMS) has long been one of the most prominent materials utilized. It is therefore unsurprising that demands for its usage has now extended beyond experimental works into computational simulations, particularly those involving finite element method (FEM). To replicate the mechanical properties of PDMS in FEM, an accurate constitutive model is required, preferably one that encompasses wide ranges of PDMS elasticity. In this study, we determine Mooney–Rivlin 5 parameters as the best hyperelastic model fitted against PDMS experimental data, and proceed to construct a parameter correlation plot combining PDMS of different elasticities together. Experimental validation using PDMS samples fabricated via 3D-printed molds is then performed using parameters extracted from this plot, showing good agreement between simulation and experimental result. In addition, to reflect model applicability, simulations related to basic mechanical deformations involved in flexible devices (compression, stretching, bending and twisting) are performed and analyzed. Further analysis is also performed to investigate the effect of combining different experimental datasets as input into the model. We expect our work to be potentially helpful to be applied as both framework and database for wearable device engineers and researchers who are experimenting with varying PDMS concentrations and modulus.

show abstract

“…Literature suggests that cell usually behaves as elastic; the common cells like human osteosarcoma cells, cell line MG63 (Procell) (Sun et al 2021), leukemia myeloid cells (HL60), human umbilical vein endothelial cells (HUVECs), 3T3 fibroblast, U2OS cells (Barreto et al 2013), HeLa cells (Wang et al 2019), RBCs, Raji, Hut, andK562 cells (Li et al 2012) have been observed to be exhibiting elastic behavior. However, cells like Living MDA-MB-231 (Tang et al 2019), REF52 (rat embryonic fibroblast) cells (Müller et al 2021), and mouse oocyte and embryo cells (Abbasi et al 2018) exhibit hyperelastic behaviors. Also, soft biological materials derived from artery and cardiac muscle (Comsol 2008), spleen, liver and kidney, breast and lung (Wex et al 2015), and brain and fat tissues (Mihai et al 2015) behave as hyperelastic materials.…”

Section: Introductionmentioning

confidence: 99%

ANN-aided Mechanophenotyping of Biological Cells Using AFM

Kamble

Raj

Thakur

2022

Preprint

View full text Add to dashboard Cite

Mechanical properties of biological cells can serve as biomarkers for indicating various diseases like cancer and sickle cell disease. Hertzian model-based prediction of mechanical properties of biological cells, although most widely used, has shown to have limited potential in determining constitutive parameters of cells of uneven shape and non-linear force-indentation responses of AFM-based cell nanoindentation. We report a new artificial neural network-aided approach, which takes into account, the variation in cell shapes and their effect on the predictions in cell mechanophenotyping. We have developed an artificial neural network (ANN) model which could predict the mechanical properties of biological cells by utilizing the force vs. indentation curve of AFM and we obtained a recall of 0.98 ± 0.03 and 1 ± 0.0 for hyperelastic and elastic cells respectively for the prediction error of less than 10%. We envisage that the developed technique can be used for the validation of quantitative biomechanical markers for diagnoses of diseases like cancer and sickle cell disease which could help to improve clinical decision-making.

show abstract

Application of Hyperelastic Models in Mechanical Properties prediction of Mouse Oocyte and Embryo Cells at Large Deformations

Cited by 5 publications

References 31 publications

Comparison of energy absorption in negative stiffness honeycombs made of Polyamide 11 and 12

Comparison of energy absorption in negative stiffness honeycombs made of Polyamide 11 and 12

Comprehensive constitutive modeling and analysis of multi-elastic polydimethylsiloxane (PDMS) for wearable device simulations

ANN-aided Mechanophenotyping of Biological Cells Using AFM

Contact Info

Product

Resources

About