Fabrication of AuPd Nanoparticles/CuNi‐BTC Metal‐Organic Framework/Electrochemically Reduced Graphene Oxide Ternary Composites for Electrochemical Detection of Dopamine

Dopamine (DA) is a key neurotransmitter whose abnormal level fluctuations are implicated in various diseases, rendering highly sensitive and selective detection critical for the early diagnosis and intervention. In this study, a bimetallic CuNi‐BTC (BTC = 1,3,5‐benzenetricarboxylic acid) metal‐organic framework (MOF) served as the carrier for AuPd bimetallic nanoparticles (AuPd NPs); the resultant hybrid was combined with graphene oxide (GO) and subjected to electrochemical reduction, producing an AuPd/CuNi‐BTC/electrochemically reduced GO (AuPd/CuNi‐BTC/ERGO) composite. The regular one‐dimensional rod‐like microstructure of the CuNi‐BTC MOF served as the structural skeleton and contributed to synergistic effects within the composite. It was an ideal carrier for uniformly dispersing the bimetallic nanoparticles, which enhanced the electrocatalytic activity toward DA. Meanwhile, the ERGO imparted high conductivity, enabling rapid interfacial electron transfer. The resulting composite was employed to modify a glassy carbon electrode for DA sensing. Experimental results demonstrated that the sensor exhibited two linear response ranges for DA, from 0.1 to 10.0 μM and from 10.0 to 400.0 μM, with a detection limit of 0.018 μM (S/N = 3) and a sensitivity of 24.53 μA μM ⁻¹  cm ⁻² . In anti‐interference tests, the presence of 10‐fold excess of common interferents (e.g., uric acid, ascorbic acid, and glucose) resulted in a current suppression of less than 10%, suggesting favorable selectivity. The sensor also exhibited acceptable reproducibility (RSD = 2.33%, n  = 5) and satisfactory long‐term stability, retaining 102.6% of its initial response after 42 days of storage at 4 °C. When applied to DA detection in urine and commercial DA injection samples, the recoveries ranged from 100.59% to 104.85% (RSD = 1.68%–5.94%), confirming its potential for practical quantitative analysis of DA.