Intrinsic proton conductive deoxyribonucleic acid (DNA) intercalated graphene oxide membrane for high-efficiency proton conduction

Ultra-Tough Graphene Oxide/DNA 2D Hydrogel with Intrinsic Sensing and Actuation Functions

Hydrogel devices with mechanical toughness and tunable functionalities are highly desirable for practical long-term applications such as sensing and actuation elements for soft robotics. However, existing hydrogels have poor mechanical properties, slow rates of response, and low functionality. In this work, two-dimensional hydrogel actuators are proposed and formed on the self-assembly of graphene oxide (GO) and deoxynucleic acid (DNA). The self-assembly process is driven by the GO-induced transition of double stranded DNA (dsDNA) into single stranded DNA (ssDNA). Thus, the hydrogel's structural unit consists of two layers of GO covered by ssDNA and a layer of dsDNA in between. Such heterogeneous architectures stabilized by multiple hydrogen bondings have Young's modulus of up to 10 GPa and rapid swelling rates of 4.0 × 10-3 to 1.1 × 10-2 s-1, which surpasses most types of conventional hydrogels. It is demonstrated that the GO/DNA hydrogel actuators leverage the unique properties of these two materials, making them excellent candidates for various applications requiring sensing and actuation functions, such as artificial skin, wearable electronics, bioelectronics, and drug delivery systems.

Graphene oxide-DNA/graphene oxide-PDDA sandwiched membranes with neuromorphic function

The behavior of polyelectrolytes in confined spaces has direct relevance to the protein mediated ion transport in living organisms. In this paper, we govern lithium chloride transport by the interface provided by polyelectrolytes, polycation, poly(diallyldimethylammonium chloride) (PDDA) and, polyanion, double stranded deoxyribonucleic acid (dsDNA), in confined graphene oxide (GO) membranes. Polyelectrolyte-GO interfaces demonstrate neuromorphic functions that were successfully applied with nanochannel ion interactions contributed, resulting in ion memory effects. Excitatory and inhibitory post-synaptic currents were tuned continuously as the number of pulses applied increased accordingly, increasing decay times. Furthermore, we demonstrated the short-term memory of a trained vs untrained device in computation. On account of its simple and safe production along with its robustness and stability, we anticipate our device to be a low dimensional building block for arrays to embed artificial neural networks in hardware for neuromorphic computing. Additionally, incorporating such devices with sensing and actuating parts for a complete feedback loop produces robotics with its own ability to learn by modifying actuation based on sensing data.