Tailored portable electrochemical sensor for dopamine detection in human fluids using heteroatom-doped...

There is widespread interest in the design of portable electrochemical sensors for the selective monitoring of biomolecules. Dopamine (DA) is one of the neurotransmitter molecules that play a key role in the monitoring of some neuronal disorders such as Alzheimer's and Parkinson's diseases. Facile synthesis of the highly active surface interface to design a portable electrochemical sensor for the sensitive and selective monitoring of biomolecules (i.e., DA) in its resources such as human fluids is highly required.

The designed sensor is based on a three-dimensional phosphorous and sulfur resembling a g-C₃N₄ hornet's nest (3D-PS-doped CNHN). The morphological structure of 3D-PS-doped CNHN features multi-open gates and numerous vacant voids, presenting a novel design reminiscent of a hornet's nest. The outer surface exhibits a heterogeneous structure with a wave orientation and rough surface texture. Each gate structure takes on a hexagonal shape with a wall size of approximately 100 nm. These structural characteristics, including high surface area and hierarchical design, facilitate the diffusion of electrolytes and enhance the binding and high loading of DA molecules on both inner and outer surfaces. The multifunctional nature of g-C₃N₄, incorporating phosphorous and sulfur atoms, contributes to a versatile surface that improves DA binding. Additionally, the phosphate and sulfate groups' functionalities enhance sensing properties, thereby outlining selectivity. The resulting portable 3D-PS-doped CNHN sensor demonstrates high sensitivity with a low limit of detection (7.8 nM) and a broad linear range spanning from 10 to 500 nM.

The portable DA sensor based on the 3D-PS-doped CNHN/SPCE exhibits excellent recovery of DA molecules in human fluids, such as human serum and urine samples, demonstrating high stability and good reproducibility. The designed portable DA sensor could find utility in the detection of DA in clinical samples, showcasing its potential for practical applications in medical settings.