Experimental study demonstrates sensitive one-drop dopamine detection in biological matrices and human sweat, indicating strong potential for non-invasive point-of-care diagnostics.
Accurate monitoring of neurotransmitters is fundamental to understanding neural communication, early diagnosis, monitoring and management of neurological disorders such as Parkinson’s disease, Alzheimer’s disease and schizophrenia. Herein, we developed a bio-derived nanocomposite-based smart one-drop sensing platform for the multifaceted detection of dopamine (DA) with high selectivity and sensitivity. The nanocomposite comprised of MXene (MX) sheets decorated with banana peel-derived carbon dots (CD) and gold nanoparticles (Au), which synergistically enhanced the surface functionality, electrocatalytic activity, selectivity and electron-transfer efficiency. Structural and morphological characterizations confirmed the successful synthesis and uniform integration of the nanocomposite components. The developed DA sensor demonstrated high sensitivity, a wide linear detection range (2-914 µM), a low detection limit (0.15 µM), and excellent selectivity against common interferents. In addition, the practical utility of the sensor was demonstrated by quantifying spiked DA in rat brain homogenate and further validated by detecting extracellular DA released from human neuroblastoma (SH-SY5Y) cells upon K + stimulation. Using this, a smartphone-integrated, portable, thumb-sized one-drop sensing device was developed for microvolume DA detection. The performance of this one-drop platform was verified by quantifying DA in human sweat droplets, confirming its suitability for non-invasive, real-time, and on-site neurochemical detection. These results confirm the successful validation of MX-CD-Au/SPE sensor in ex vivo, in vitro, and one-drop sensing environments. Overall, this MX-CD-Au based one-drop sensing system represents a new generation of sustainable, miniaturized platforms for real-time neurotransmitter monitoring, effectively bridging the gap between conventional laboratory assays and wearable, point-of-care diagnostics.
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Theyagarajan et al. (2026) studied this question.
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