Enzymeless copper microspheres@carbon sensor design for sensitive and selective acetylcholine screening in human serum

Follow up of neuronal disorders, such as Alzheimer's and Parkinson's diseases using simple, sensitive, and selective assays is urgently needed in clinical and research investigation. Here, we designed a sensitive and selective enzymeless electrochemical acetylcholine sensor that can be used in human fluid samples. The designed electrode consisted of a micro spherical construction of Cu-metal decorated by a thin layer of carbon (CuMS@C). A simple and one-pot synthesis approach was used for Cu-metal controller formation with a spherical like structures. The spherical like structure was formed with rough outer surface texture, circular build up, homogeneous formation, micrometric spheres size (0.5 -1 mu m), and internal hollow structure. The formation of a thin layer of carbon materials on the surface of CuMS sustained the catalytic activity of Cu atoms and enriched negatively charge of the surface. CuMS@C acted as a highly active mediator surface that consisted of Cu metal as a highly active catalyst and carbons as protecting, charge transport, and attractive surface. Therefore, the CuMS@C surface morphology and composition played a key role in various aspects such as facilitated ACh diffusion/loading, increased the interface surface area, and enhanced the catalytic activity. The CuMS@C acted as an electroactive catalyst for ACh electrooxidation and current production, due to the losing of two electrons. The fabricated CuMS@C could be a highly sensitive and selective enzymeless sensor for detecting ACh with a detection limit of 0.1 mu M and a wide linear range of 0.01 - 0.8 mM. The designed ACh sensor assay based on CuMS@C exhibited fast sensing property as well as sensitivity, selectivity, stability, and reusability for detecting ACh in human serum samples. This work presents the design of a highly active electrode surface for direct detection of ACh and further clinical investigation of ACh levels.