Hybrid FEM-ANN Framework for Multi-Mass Detection on Graphene Nanoresonators

Graphene's remarkable stiffness, extremely low density, and large surface-to-mass ratio make it highly responsive to minute mass variations, even down to the zeptogram scale. Although most prior studies have concentrated on single-particle adsorption, practical scenarios often involve multiple particles randomly distributed on the surface, leading to complex nonlinear vibration behaviors. In this work, a hybrid FEM-ANN framework is developed that integrates high-fidelity finite element simulations with a deep-learning neural network to analyze the vibrational response of graphene sheets under multi-mass loading. The dataset generated from simulations-incorporating variations in aspect ratio, sheet dimensions, number of particles, and their random distribution-is used to train and validate the neural network. The trained model accurately predicts nonlinear frequency shifts with substantially reduced computational cost compared to full numerical simulations. This hybrid approach provides a reliable and efficient tool for rapid multi-mass detection in graphene nanoresonators, with potential applications in biosensing and environmental monitoring.

کلیدواژه ها:

graphene ، Frequency response ، Finite element modeling ، mass sensing