The inherent properties of 2D materials—light mass, high out-of-plane flexibility, and large surface area—promise great potential for precise and accurate nanomechanical mass sensing, but their application is often hampered by surface contamination. Here we demonstrate a tri-layer graphene nanomechanical resonant mass sensor with sub-attogram resolution at room temperature, fabricated by a bottom-up process. We found that Joule-heating is effective in cleaning the graphene membrane surface, which results in a large improvement in the stability of the resonance frequency. We characterized the sensor by depositing Cr metal using a stencil mask and found a mass-resolution that is sufficient to weigh very small particles, like large proteins and protein complexes, with potential applications in the fields of nanobiology and medicine.

Nanomechanical resonator devices are widely used as ultrasensitive mass detectors for fundamental studies and practical applications. The resonance frequency of the resonators shifts when a mass is loaded, which is used to estimate the mass. However, the shift signal is often blurred by the thermal noise, which interferes with accurate mass detection. Here, we demonstrate the reduction of the noise interference in mass detection in suspended graphene-based nanomechanical resonators, by using applied machine learning. Featurization is divided into image and sequential datasets, and those datasets are trained and classified using 2D and 1D convolutional neural networks (CNNs). The 2D CNN learning-based classification shows a performance with f1-score over 99% when the resonance frequency shift is more than 2.5% of the amplitude of the thermal noise range.

Mechanical resonance properties of porous graphene resonators were investigated by simulation studies. The finite element method was utilized to design the porous graphene membrane pattern and to calculate the mechanical resonance frequency and quality factor. The changes in the resonance frequency and quality factor were systematically studied by changing the size, number, and relative location of pores on the graphene membrane. Mass loss and carbon-carbon bond break were found to be the main competing parameters for determining its mechanical resonance properties. The correlation between the geometry and the damping effect on the mechanical resonance of graphene was considered by suggesting a model on the damping factor and by calculating the membrane deflections according to the pore location. Based on the simulation results, an optimal porosity and porous geometry were found that gives the maximum resonance frequency and quality factor. Suspended graphene with various number pore structures was experimentally realized, and their mechanical resonance behaviors were measured. The trend of changes in resonance frequency and quality factor according to the number of pores in the experiment was qualitatively agreed with simulation results.