Strong internal resonance in a nonlinear, asymmetric microbeam resonator

Asadi, Keivan; Yeom, Junghoon; Cho, Hanna

doi:10.1038/s41378-020-00230-1

Cited by 26 publications

(16 citation statements)

References 61 publications

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“…Interestingly, at the maximum drive level (10 dBm), the strong coupling between the resonant modes yields the emergence of a third peak in the middle of the split region at frequency f IR = 22.73 MHz (see Figure 2-c). This unexpected additional peak at f IR was not observed in nonlinear resonators undergoing a similar IR [29,30].…”

Section: Experimental Characterization Of Frequency Combmentioning

confidence: 79%

Symmetry-breaking induced frequency combs in graphene resonators

Keşkekler¹,

Arjmandi²,

Steeneken³

et al. 2022

Preprint

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Nonlinearities are inherent to the dynamics of two-dimensional materials. Phenomena like intermodal coupling already arise at amplitudes of only a few nanometers, and a range of unexplored effects still awaits to be harnessed. Here, we demonstrate a route for generating mechanical frequency combs in graphene resonators undergoing symmetry-breaking forces. We use electrostatic force to break the membrane's out-of-plane symmetry and tune its resonance frequency towards a two-to-one internal resonance, thus achieving strong coupling between two of its mechanical modes. When increasing the drive level, we observe splitting of the fundamental resonance peak, followed by the emergence of a frequency comb regime. We attribute the observed physics to a non-symmetric restoring potential, and show that the frequency comb regime is mediated by a Neimark bifurcation of the periodic solution. These results demonstrate that mechanical frequency combs and chaotic dynamics in 2D material resonators can emerge near internal resonances due to symmetry-breaking.

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Section: Experimental Characterization Of Frequency Combmentioning

confidence: 79%

Symmetry-breaking induced frequency combs in graphene resonators

Keşkekler¹,

Arjmandi²,

Steeneken³

et al. 2022

Preprint

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“…To better demonstrate the saturation phenomenon, we refer to the force-response curve 13 – 15 , 18 . Figure 7 show the responses of the 1st and 2nd modes when varying the AC excitation obtained through experiments and numerical simulations.…”

Section: Resultsmentioning

confidence: 99%

“…Internal resonances and saturation phenomenon have been observed and studied for MEMS resonators 15 – 18 . Recently, this concept has been proposed for frequency stabilization and noise reduction in MEMS resonators 19 – 23 , which is shown to be promising for frequency fluctuation reduction for lower vibration modes through the excitation of higher-order modes 23 .…”

Section: Introductionmentioning

confidence: 99%

Nonlinear mode saturation in a U-shaped micro-resonator

Rocha

Younis

2022

Sci Rep

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Saturation is an intriguing phenomenon that has captured the attention of scientists since the time of Froude when he reported it for ship motion in the mid of the nineteenth century. This work presents the demonstration and a comprehensive study of the nonlinear saturation phenomenon on a compound micromachined structure of U-shape (micro portal frame). The frame is designed and fabricated as a multi-input and multi-output device for actuating the 1st (sway) and 2nd (symmetric) in-plane vibration modes. Geometric nonlinearities along with the softening effect of the electrostatic force present the necessary conditions for the activation of a 2:1 internal (auto-parametric) resonance between the 1st and 2nd modes. Experimental data complemented with analytical simulations are obtained showing the internal resonance and the saturation phenomenon. These results are promising for further exploration of such compound structures and for further in-depth studies of the saturation phenomenon on a variety of other systems and applications.

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“…The results of the macroscopic analysis suggest that a strong nonlinear response is obtained in the basal ganglia network, similar to resonating mechanical systems e.g. (Asadi et al 2021;Bureau et al 2014Bureau et al , 2013Schilder et al 2015). The following analysis proposes optimal frequencies above 130Hz, suggesting the investigation of DBS treatment with frequencies beyond 130Hz in animal experiments.…”

Section: Predicting Network Dynamics Under Dbs Using Computational Approachesmentioning

confidence: 87%

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model

et al. 2021

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A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities are derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities (synchronisation index, mean synaptic activity and response efficacy) switch from normal to Parkinsonian conditions. Simulating DBS of the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and Parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.

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Strong internal resonance in a nonlinear, asymmetric microbeam resonator

Cited by 26 publications

References 61 publications

Symmetry-breaking induced frequency combs in graphene resonators

Symmetry-breaking induced frequency combs in graphene resonators

Nonlinear mode saturation in a U-shaped micro-resonator

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model

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