Devarajan, K. (ECE) – Towards Multimodal Detection of Neuronal Signaling using Graphene Heterostructures

Understanding neuronal function requires measurements that capture both the rapid dynamics and spatial organization of neural activity. Conventional neural interfaces often prioritize either high temporal resolution through electrophysiological recording or high spatial resolution through optical imaging, while integrating these modalities within a single interface remains challenging. This work seeks to develop a transparent, flexible, and biocompatible neural interface that enables complementary electrical and optical measurements of neuronal activity.

We propose a graphene-based heterostructure neural interface that leverages the electrical, optical, and mechanical properties of graphene to enable electrophysiological sensing while maintaining optical access to the underlying neural system. The device incorporates a bowtie geometry that transitions from multilayer to single-layer graphene, with the multilayer graphene regions serving as conductive leads and the single-layer graphene defining the primary sensing region. This architecture provides electrical connectivity while maximizing optical access in the region of interest, establishing a platform for multimodal interrogation of neuronal activity.

Preliminary measurements using simulated neuronal signals demonstrate detection of transient electrophysiological signals approximately 1 ms in duration and with amplitudes up to 100 μV, establishing the feasibility of the device for resolving physiologically relevant neural signals. Building on these results, the proposed work will extend the platform toward biological systems through interfacing with neuronal organoids and development of multichannel recording capabilities. Finally, the established heterostructure architecture will provide a framework for investigating alternative two-dimensional sensing materials, including MoS₂, with the goal of understanding how material properties influence electrophysiological signal detection while preserving optical accessibility.

Ultimately, this work aims to establish a scalable 2D-material-based neural interface that integrates electrophysiological sensing with optical access, enabling multimodal characterization of neuronal activity across complementary spatial and temporal scales.

Event Host: Koushik Devarajan, Ph.D. Student, Electrical and Computer Engineering 

Advisor: Tal Sharf