Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

McCraw, Marshall R.; Uluutku, Berkin; Solomon, Halen D; Anderson, Megan S.; Sarkar, Kausik; Solares, Santiago D.

doi:10.1039/d2sm01331b

Soft Matter

2023

DOI: 10.1039/d2sm01331b

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Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Marshall R. McCraw

Berkin Uluutku

Halen D Solomon

et al.

Abstract: Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range...

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Machine learning assisted multifrequency AFM: Force model prediction

Elsherbiny,

Santos,

Gadelrab

et al. 2023

Applied Physics Letters

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Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, to predict the optimum force model from the observables of multifrequency AFM pixel by pixel. This approach allows distinguishing between different phenomena and selecting a suitable force model directly from observables. We generate predictive models using simulation data. Finally, the formalism of multifrequency AFM can be employed to analytically recover material properties by inputting the right force model.

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Machine learning assisted multifrequency AFM: Force model prediction

Elsherbiny,

Santos,

Gadelrab

et al. 2023

Applied Physics Letters

View full text Add to dashboard Cite

show abstract

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Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Abstract: Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range...

Cited by 1 publication

References 104 publications

Machine learning assisted multifrequency AFM: Force model prediction

Machine learning assisted multifrequency AFM: Force model prediction

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