Hybrid graphene-MXene heterostructure for ultra-sensitive optomechanical sensors with multi-modal robotic sensing capabilities

Abstract

This work presents a computational investigation of a hybrid graphene-MXene heterostructure designed as an optomechanical sensor for robotic applications, integrating photonic detection with mechanical sensing in a unified platform. All results reported herein are derived exclusively from multi-scale simulations; no experi mental validation has been performed. The proposed architecture exploits the broadband optical absorption of graphene and the plasmonic response of Ti 3 C 2 ◦ T x MXene in the visible spectrum. Density functional theory (DFT) calculations coupled with finite element modeling (FEM) predict a photoresponsivity of 1.2 A/W at 550 nm, detectivity exceeding 1012 Jones, and spectral coverage across 400–800 nm. Simulated optical and mechanical response times are 2.1 ms and 2.8 ms, respectively, enabling concurrent multi-modal operation. Dual-gate voltage control allows independent tuning of photonic and piezoresistive channels. Predicted pressure sensi tivity reaches 4.2 kPa⁻1 in the low-force regime (0.05–1 kPa), with additional environmental responsivity of 0.18%/ C (temperature) and 0.3%/RH (humidity, 20–90% range). Iterative FEM simulations over 50,000 loading cycles suggest minimal performance degradation under idealized conditions. The heterostructure’s multi-modal capability—decoupled through differential material responses—enables simultaneous optical transparency analysis, texture recognition via combined optical-mechanical signatures, and low-light navigation. This theoretical framework establishes performance benchmarks for integrated optomechanical sensors in autonomous robotics, human-robot interaction, and industrial automation, while highlighting the necessity of experimental validation to assess practical feasibility, interface stability, and environmental robustness.