Machine learning to probe modal interaction in dynamic atomic force microscopy

Modal interactions are pervasive effects that commonly emerge in nanomechanical systems. The coupling of vibrating modes can be leveraged in many ways, including to enhance sensing or to disclose complex phenomenologies. In this work we show how machine learning and data-driven approaches could be used to capture intermodal coupling. We...

High-frequency stochastic switching of graphene resonators near room temperature

Stochastic switching between the two bistable states of a strongly driven mechanical resonator enables detection of weak signals based on probability distributions, in a manner that mimics biological systems. However, conventional silicon resonators at the microscale require a large amount of fluctuation power to achieve a switching rate in the...

Nonlinear dynamic identification of graphene's elastic modulus via reduced order modeling of atomistic simulations

Despite numerous theoretical investigations on the mechanical properties of graphene, an accurate identification of its material behavior is still unattained. One hypothesis for this uncertainty is that modeling graphene as a static membrane cannot describe the strong coupling between mechanics and thermodynamics of this structure. Therefore,...

Nonlinear dynamics for estimating the tip radius in atomic force microscopy

The accuracy of measurements in Amplitude Modulation Atomic Force Microscopy (AFM) is directly related to the geometry of the tip. The AFM tip is characterized by its radius of curvature, which could suffer from alterations due to repetitive mechanical contact with the surface. An estimation of the tip change would allow the user to assess...