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norskBlar i forfatter Fakultet for naturvitenskap og teknologi "Gadelrab, Karim"
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Machine learning assisted multifrequency AFM: Force model prediction
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-05)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, ... -
Probing power laws in multifrequency AFM
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-17)Quantification of conservative forces in multifrequency atomic force microscopy requires solving the general equations of the theory expressed in terms of the virials of interaction. Power law expressions are commonly utilized when dealing with electrostatic, ferroelectric, magnetic, or long range (van der Waals) forces. Here, we discuss long range forces modeled in terms of power laws (n), where ... -
Quantification of van der Waals forces in bimodal and trimodal AFM
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-22)The multifrequency formalism is generalized and exploited to quantify attractive forces, i.e., van der Waals interactions, with small amplitudes or gentle forces in bimodal and trimodal atomic force microscopy (AFM). The multifrequency force spectroscopy formalism with higher modes, including trimodal AFM, can outperform bimodal AFM for material property quantification. Bimodal AFM with the second ...
