Abstract

The precise detection and measurement of dopamine (DA), a crucial neurotransmitter in the human body, plays a significant role in diagnosing, preventing, and treating neurological diseases associated with its levels. A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots (MSQDs) with multiwalled carbon nanotubes (MWCNTs). The MSQDs were synthesized using the shear exfoliation method. The sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction, and MWCNTs amplify the sensor response. The linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid (AA, 50 µmol·L‒1) and uric acid (UA, 200 µmol·L‒1), and exhibited linearity from 2 to 966 µmol·L‒1 of DA with 0.097 µA(mol·L‒1)‒1 sensitivity and a low limit of detection of 0.6 µmol·L‒1 (the ratio between signal and noise, S/N = 3). Moreover, the sensitivity and selectivity of the sensor were also studied using chronoamperometry. There is no increase in amperometric current after adding the most potentially interfering biomolecules. The sensor was successfully applied to recover DA in human blood sera samples. Further, machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and UA. These algorithms facilitate the identification and quantification of DA amidst coexisting interferents, including AA, that are commonly present in biological matrices.

Recommended citation: Geethukrishnan, Apte, O. & Tadi, K.K. Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability. Tungsten 7, 137–151 (2025). https://doi.org/10.1007/s42864-024-00297-7 https://link.springer.com/article/10.1007/s42864-024-00297-7