Self-stacking of exfoliated charged nanosheets of LDHs and graphene as biosensor with real-time tracking of dopamine from live cells

Abstract

This study introduces a new strategy for periodic stacking of positively charged NiAl layered double hydroxides (LDHs) nanosheets with negatively charged monolayers of graphene (G) by systematically optimizing several parameters in a controlled co-feeding fashion and resultant heterostacked NiAl LDH/G LBL nanocomposites have been practically applied in sensitive detection of dopamine released from live cells as early Parkinson's disease (PD) diagnostic tool. PD is the second most chronic neurodegenerative disorder with gradual progressive loss of movement and muscle control causing substantial disability and threatening the life seriously. Unfortunately majority of dopaminergic neurons present in substantia nigra of PD patients are destroyed before it is being clinically diagnosed, so early stages PD diagnosis is essential. Because of direct neighboring of extremely conductive graphene to semiconductive LDHs layers, enhanced intercalation capability of LDHs, and huge surface area with numerous active sites, good synergy effect is harvested in heteroassembled NiAl LDH/G LBL material, which in turn shows admirable electrocatalytic ability in DA detection. The interference induced by UA and AA is effectively eliminated especially after the modifying the electrode with Nafion. The outstanding electrochemical sensing performance of NiAl LDH/G LBL modified electrode has been achieved in terms of broad linear range and lowest real detection limit of 2 nM (S/N = 3) towards DA oxidation. Benefitting from superior efficiency, biosensor has been successfully used for real-time in-vitro tracking of DA efflux from live human nerve cell after being stimulated. We believe that our biosensing platform of structurally integrated well-ordered LBL heteroassembly by inserting graphene directly to the interlayer galleries of LDHs material will open up new avenue in diseases determination window.