A dual-mode photoelectrochemical/ratiometric fluorescence sensing platform for dopamine detection based on a ternary MEG heterostructure.
Abstract
Self-validated bioanalytical systems based on mechanistically orthogonal dual-mode transduction have attracted considerable interest, yet their development critically depends on multifunctional heterostructure materials that simultaneously enable efficient charge separation and well-resolved dual-emission output. Herein, a dual-mode photoelectrochemical/ratiometric fluorescence (PEC/FL) platform was constructed for sensitive dopamine (DA) determination based on a ternary MXene/Eu,Gd-CQDs/B,GQDs-g-C3N4 heterostructure (MEG). The MXene-mediated cascade interfacial architecture enabled efficient separation and rapid transport of photogenerated charge carriers for PEC signal amplification, while retaining well-resolved and reproducible dual-emission characteristics suitable for ratiometric fluorescence detection. In the PEC mode, the photocurrent increased with DA concentration, giving two separated linear calibration ranges of 0.01-0.20 μM and 10-500 μM, with a limit of detection (LOD) of 2.8 nM. In the FL mode, DA produced a wavelength-dependent ratiometric response dominated by progressive quenching of the 445 nm emission, yielding a concentration-dependent F365/F445 ratiometric response over 0.05-1.0 μM with an LOD of 38 nM. The complementary transduction mechanisms enabled mutual verification between the two modes, thereby reducing the risk of false-positive or false-negative interpretation associated with reliance on a single readout. Both modalities demonstrated excellent selectivity toward DA over common coexisting species, together with satisfactory operational stability and reproducibility. Practical applicability was validated in fetal bovine serum and river water samples, yielding recoveries of 96.8-104.2% with relative standard deviations below 5%. These results demonstrate that the MEG-based dual-readout strategy offers a robust, self-validated approach to reliable bioanalysis in complex matrices, using DA as a model system.