Exploration of Hybrid Carbon Materials Through Molecular Dynamics Simulations-Guided Engineering of Polymers

The current state-of-the-art research in neural probes predominantly concentrates on either increasing channel counts for electrophysiology, increasing longevity of the implanted microelectrodes, or increasing the voltammetric sensitivity of microelectrodes for neurochemistry applications. These siloed approaches that focus largely on only <b><i>single modality</i></b> at a time have slowed down progress in understanding of large-scale brain circuitry, particularly in short- and long-term behavior studies. From a microelectrode material point of view, carbon-based electrodes, specifically glassy carbon (GC) and graphene (Gr), have been of significant interest in addressing the research needs described above. For example, GC microelectrodes have been shown to offer a compelling platform for detection of neurotransmitters such as dopamine and serotonin at record sensitivities (10 nM). However, there are still some key factors that continue to offer obstacles to increased innovations in these carbon materials like the single-atomic layer nature of graphene that renders handling and processing of devices difficult and relatively low electrical conductivity and slow charge mobility of glassy carbon <b>along with </b>non-uniform and sporadic distribution of chemically active functional groups in both Gr and GC that is needed to promote surface chemistry for biomolecule adsorption.<br/>Driven by the need for addressing these gaps through the discovery of newer materials, we introduce a new approach for engineering of electrical, electrochemical, and surface properties of carbon (particularly two of its allotropes, Gr and GC), through a molecular dynamic (MD) modeling of polymer precursors, with the specific target of synthesizing a <b>hybrid material </b>that is made of flat <i>graphene-like</i> <i>6-member</i> carbon rings (high conductivity and charge mobility) and 3D cage-like carbon nanostructures (high charge storage capacity. The modeling work is supported by synthesis of a hybrid material that exhibits enhanced conductivity, charge mobility, and charge storage capacity.