Practical assessment of nanoscale indentation techniques for the biomechanical properties of biological materials

Chang, Alice C.; Liao, Juinn Der; Liu, Bernard Haochih

doi:10.1016/j.mechmat.2016.03.005

Cited by 18 publications

(5 citation statements)

References 85 publications

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“…Furthermore, the mechanical properties of the cortical bone depend also on other factors such as the anatomical zone of the tested sample, sex, age, the hydration state of the material, the probe geometry, etc... [25,33,45,49,58]. The finite element approach has thus become essential for determining the mechanical properties of materials such as the cortical bone [8,12,22], particularly their time-dependent mechanical properties such as creep, viscoelasticity or elastoplasticity [12,14].…”

Section: Introductionmentioning

confidence: 99%

Application of the Johnson-Cook plasticity model in the finite element simulations of the nanoindentation of the cortical bone.

Remache

Semaan

Rossi

et al. 2020

Journal of the Mechanical Behavior of Biomedical Materials

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The mechanical behavior of the cortical bone in nanoindentation is a complicated mechanical problem. The finite element analysis has commonly been assumed to be the most appropriate approach to this issue. One significant problem in nanoindentation modeling of the elastic-plastic materials is pileup deformation, which is not observed in cortical bone nanoindentation testing. This phenomenon depends on the work-hardening of materials; it doesn't occur for work-hardening materials, which suggests that the cortical bone could be considered as a work-hardening material. Furthermore, in a recent study [59], a plastic hardening until failure was observed on the micro-scale of a dry ovine osteonal bone samples subjected to micropillar compression. The purpose of the current study was to apply an isotropic hardening model in the finite element simulations of the nanoindentation of the cortical bone to predict its mechanical behavior. The Johnson-Cook (JC) model was chosen as the constitutive model. The finite element modeling in combination with numerical optimization was used to identify the unknown material constants and then the finite element solutions were compared to the experimental results. A good agreement of the numerical curves with the target loading curves was found and no pileup was predicted. A Design Of Experiments (DOE) approach was performed to evaluate the linear effects of the material constants on the mechanical response of the material. The strain hardening modulus and the strain hardening exponent were the most influential parameters. While a positive effect was noticed with the Young's modulus, the initial yield stress and the strain hardening modulus, an opposite 1 effect was found with the Poisson's ratio and the strain hardening exponent. Finally, the JC model showed a good capability to describe the elastoplastic behavior of the cortical bone.

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Section: Introductionmentioning

confidence: 99%

Application of the Johnson-Cook plasticity model in the finite element simulations of the nanoindentation of the cortical bone.

Remache

Semaan

Rossi

et al. 2020

Journal of the Mechanical Behavior of Biomedical Materials

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“…With the capability to capture the high‐resolution image, AFM can also detect the mechanical properties of the specimen by monitoring the interaction between the probe and sample surface (Sahin & Erina, ; Dokukin & Sokolov, ; Chang et al. , ). Graham et al .…”

Section: Introductionmentioning

confidence: 99%

“…With the capability to capture the high-resolution image, AFM can also detect the mechanical properties of the specimen by monitoring the interaction between the probe and sample surface (Sahin & Erina, 2008;Dokukin & Sokolov, 2012;Chang et al, 2016). Graham et al (2010) imaged the ultrastructure of the native biomolecules, which were different types of the collagen, among the sectioned tissue by AFM and connected the observation from different microscopes.…”

Section: Introductionmentioning

confidence: 99%

Structure‐dependent behaviours of skin layers studied by atomic force microscopy

Chang

Liu

Shao

et al. 2017

Journal of Microscopy

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The multilayer skin provides the physical resistance and strength against the environmental attacks, and consequently plays a significant role in maintaining the mammalian health. Currently, optical microscopy (OM) is the most common method for the research related to skin tissues while with the drawbacks including the possibility of changing the native morphology of the sample with the addition of the chemical or immunological staining and the restricted resolution of images for the direct observation of the tissue structures. To investigate if the function of each tissue is structure-dependent and the how the injured skin returns to the intact condition, we applied atomic force microscopy (AFM) on the sectioned mice-skin to reveal the tissue structures with a nanoscale resolution. From the outermost stratum to the inner layer of the skin tissue, the respectively laminated, fibrous, and brick-like structures were observed and corresponded to various functions. Due to the mechanical differences between the tissue constituents and their boundaries, the sizes and arrangements of the components were characterised and quantified by the mechanical mapping of AFM, which enabled the analytical comparisons between tissue layers. For the wound model, the skin tissues were examined with the initial formation of blood vessels and type-I collagen, which agreed with the stage of healing process estimated by OM but showed more detail information about the evolution of proteins among the skin. In conclusion, the characterisation of the components that consist of skin tissue by AFM enables the connection of the tissue function to the corresponded ultrastructure.

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“…The uncertainty of the nanomechanical properties obtained in AFM-based measurement arises from the degradation of the tip geometry [32,33]. The change in the tip geometry results in the variation of tip-sample interaction characteristics, increasing challenges to determining an appropriate contact model for the determination of elastic modulus.…”

Section: Current Challenges In Data Acquisitionmentioning

confidence: 99%

Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology

Nguyen¹,

Liu²

2022

Surf. Topogr.: Metrol. Prop.

Self Cite

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Scanning probe microscopy (SPM) is an outstanding nanometrology tool for characterizing the structural, electrical, thermal, and mechanical properties of materials at the nanoscale. However, many challenges remain in the use of SPM. Broadly speaking, these challenges are associated with the acquisition of the SPM data and the subsequent analysis of this data, respectively. Both problems are related to the inherent uncertainty of the data obtained in SPM-based measurements due to the nanoscale geometry of the SPM probe tip, the state of the sample imaging region, the data analysis methods themselves, and the experience of the users. Machine learning (ML) approaches have been increasingly applied to address these problems in recent years. In general, ML approaches involve constructing a well-organized and representative SPM dataset from experimental and theoretical trials, and then using the data features of this dataset for ML models to learn and produce appropriate predictions. Herein, this review examines the development of recent ML strategies for reducing measurement uncertainty in SPM-based measurements. The review commences by introducing the ML models and algorithms commonly used in SPM-related applications. Recent approaches for collecting and preprocessing the SPM data to extract significant data features for further ML processing are then introduced. A review of recent proposals for the applications of ML to the improvement of SPM instrumentation and the enhancement of data processing and overall understanding of the material phenomena is then presented. The review concludes by presenting brief perspectives on future opportunities and open challenges in the related research field.

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Practical assessment of nanoscale indentation techniques for the biomechanical properties of biological materials

Cited by 18 publications

References 85 publications

Application of the Johnson-Cook plasticity model in the finite element simulations of the nanoindentation of the cortical bone.

Application of the Johnson-Cook plasticity model in the finite element simulations of the nanoindentation of the cortical bone.

Structure‐dependent behaviours of skin layers studied by atomic force microscopy

Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology

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