Nandu Koripally1,Dmitry Kireev1,Deji Akinwande1
The University of Texas at Austin1
Nandu Koripally1,Dmitry Kireev1,Deji Akinwande1
The University of Texas at Austin1
Epidermal electronic systems such as graphene tattoos have shown great potential as elements of next-generation bioelectronics interfaces. Imperceptible monitoring of human physiological signals, such as brain activity (EEG), muscle activity (EMG), heart activity (ECG), or ocular activity (EOG), has already been shown as achievable using those atomically thin tattoos. However, one substantial drawback exists in those systems: susceptibility of recorded signal to noise. Currently, this signal amplification is done approximately ~1m away from the measurement site, and the surrounding electromagnetic fields and movement-related artifacts induce noise and artifacts in the final recorded signals. Hence, it is essential to pre-amplify the recorded signals right at the spot and right at the time of signal acquisition. This can be achieved by means of field-effect transistors known for their signal amplification capabilities, yet the on-body operation of transistors is challenging.<br/>In this work, we report our finding on a unique system that can be used to study 2D material’s properties in a mechanically soft, squishy, and flexible environment that is more realistic for future wearable applications. The reported system consists of commercially available medical-grade gel electrodes. The gel provides electrolytic interface, and the 2D material such as graphene is simply placed on top of the gel. The gel electrolytes are extremely robust towards mechanical strain, flexible, feature insignificant gate leakage currents, and are highly stable in a broad temperature range. The gel-gated graphene transistor tattoo hybrids can also be placed onto the skin for an entirely wearable modulation of graphene channel conductivity through body-mediated field effect. This reported modulation of graphene conductivity opens up new roads in wearable bioelectronics, especially in low-noise EEG monitoring.