Machine learning to probe modal interaction in dynamic atomic force microscopy

Belardinelli, Pierpaolo; Chandrashekar, Abhilash; Wiebe, Richard; Alijani, Farbod; Lenci, Stefano

doi:10.1016/j.ymssp.2022.109312

Cited by 6 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [15] the authors showed how machine learning and data-driven approaches could be used to capture intermodal coupling. We employ a quasi-recurrent neural network (QRNN) for identifying mode coupling and verifying its applicability on experimental data obtained from tapping mode atomic force microscopy (AFM).…”

Section: Atomic Force Microscopy: a State Of The Artmentioning

confidence: 99%

“…In the paper by Ref. [15], the authors investigate the mechanism of atomic force microscopy in tapping mode (AFM-TM) under the Casimir and van der Waals (VdW) force; 0-1 test was implemented to analyze the dynamics of the system, allowing the identification of the chaotic and periodic regimes of the AFM system. The dynamic results of the conventional derivative and fractional models reveal the need for the application of control techniques, such as Optimum Linear Feedback Control (OLFC), state-dependent Riccati equations (SDRE) by using feedback control, and the timedelayed feedback control.…”

Section: Atomic Force Microscopy: a State Of The Artmentioning

confidence: 99%

“…where wx , t ðÞ is microcantilever beam deflection, ux , t ðÞ is a relative deflection of displacement of the actuator, described as yt ðÞ¼Y sin Ωt ðÞ . We consider the term viscoelastic to the AFM system considering z [15,17]:…”

Section: Andmentioning

confidence: 99%

See 2 more Smart Citations

MEMS-Based Atomic Force Microscope: Nonlinear Dynamics Analysis and Its Control

Ribeiro¹,

Balthazar²,

Tusset³

et al. 2024

Chaos Monitoring in Dynamic Systems - Analysis and Applications

View full text Add to dashboard Cite

In this chapter, we explore a mathematical modelling that describes the nonlinear dynamic behavior of atomic force microscopy (AFM). We propose two control techniques for suppressing the chaotic motion of the system. The proposed model considers the interatomic interactions between the analyzed sample and the cantilever. These acting forces are van der Waals type, and we add a mathematical term that is a simple approximation to the viscoelasticity that possibly occurs in biological samples. We analyzed the behavior of the initial conditions of the proposed mathematical model, which showed a degree of complexity of the basins of attraction that were detected by entropy and uncertainty parameter, both detect if the basins have a fractal behavior. Numerical results showed that the nonlinear dynamic behavior has chaotic regions with the Lyapunov exponent, bifurcation diagram, and the Poincaré map. And, we propose two control techniques to suppress the chaotic movement of the AFM cantilever. First technique is the optimal linear feedback control (OLFC), which does not consider the nonlinearities of mathematical model. On the other hand, the control state dependent Riccati equation (SDRE) considers the nonlinearities of mathematical model. Both control techniques for a desired periodic orbit proved to be efficient.

show abstract

Section: Atomic Force Microscopy: a State Of The Artmentioning

confidence: 99%

Section: Atomic Force Microscopy: a State Of The Artmentioning

confidence: 99%

See 1 more Smart Citation

MEMS-Based Atomic Force Microscope: Nonlinear Dynamics Analysis and Its Control

Ribeiro¹,

Balthazar²,

Tusset³

et al. 2024

Chaos Monitoring in Dynamic Systems - Analysis and Applications

View full text Add to dashboard Cite

show abstract

“…26 There are many examples in the literature of using ML methods to process a lot of AFM data or improve their quality. 27,28 In our previous research, autocorrelation function (ACF) and topological data analysis (TDA) were applied to an ultrasonically treated system of Cu−Zn alloy 29,30 and thin porous films of WO 3 . 31 Several calculated parameters (stand-ard deviation of the rough profile height (1), autocorrelation length (ACL), and analysis of the extreme point location) present a topography analysis of surface roughness on different spatial scales.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Müller et al introduced a software package Nanite, which automated all basic aspects of FD data analysis, including data import, tip–sample separation, baseline correction, contact point retrieval, automation of the sorting, and model fitting for biological samples . There are many examples in the literature of using ML methods to process a lot of AFM data or improve their quality. , …”

Section: Introductionmentioning

confidence: 99%

Topological Data Analysis of Nanoscale Roughness of Layer-by-Layer Polyelectrolyte Samples Using Machine Learning

Aglikov,

Aliev,

Zhukov

et al. 2023

ACS Appl. Electron. Mater.

View full text Add to dashboard Cite

The surface roughness of layer-by-layer (LbL) polyelectrolytes is studied by atomic force microscopy (AFM) and analyzed with novel methods including topological data analysis (TDA) and machine learning (ML) to correlate multiscale roughness with the number of bilayers and to recognize the types of polyelectrolytes (PEs). LbL PEs composed of one to four bilayers of ( 1) polyethylenimine (PEI)/poly(sodium 4-styrenesulfonate) (PSS), (2) PEI/poly(acrylic acid) (PAA), and (3) PEI/ MXene rigid flakes are deposited on a smooth silicon wafer. With a growing number of bilayers, the roughness changes from a smooth surface to an equilibrium rough profile. The AFM study of the surface morphology demonstrates that surface roughness is multiscale, with smaller features imposed on larger ones. Roughness data is filtered from measurement resolution artifacts, and several methods are applied: correlation length, statistics of the distribution of extremes in trimmed images, and TDA barcodes and persistence diagrams of simplexes in 8D data space. An ML algorithm is used to determine the number of bilayers in a PE. Roughness analysis indicates a gradual transition from a smooth to a rough surface with saturation at three to four bilayers and the existence of multiscale roughness invariance.

show abstract