Fern-Shaped Au Nanostructures for the Detection of Depression-Related Metabolites Using Integrated Electrochemical Molecular-Imprinting Sensors

Multiplexed profiling of depression-related metabolites in biofluids is essential for precise screening. However, their vast concentration disparities challenge simultaneous detection using a single type of sensor. To address this challenge, this work presents an electrochemical flexible sensing strategy based on a PET/Ti/Au/AuNS/molecularly imprinted polymer (MIP) configuration. To ensure clinical relevance, the sensor’s linear ranges were tailored to align with physiological concentrations in biofluids. Supported by the fern-shaped Au nanostructures, the device achieves a detection range (1–100 μM, LOD = 0.612 μM) for l -tryptophan that covers human serum and sweat levels (15–40 μM). Furthermore, the sensor effectively monitors related metabolites with ranges optimized for their respective physiological baselines: 100–800 μM (LOD = 5.01 μM) for glutamine (encompassing healthy plasma levels of 550–750 μM) and a high-sensitivity range of 1–10 μM (LOD = 0.148 μM) for γ-aminobutyric acid to address its trace abundance in peripheral fluids due to the blood–brain barrier (0.1–0.6 mg/g in brain tissue). Building upon this, the fabricated integrated MIP-based flexible sensor successfully achieved simultaneous detection across disparate concentration gradients of key depression-related metabolites: l -tryptophan (3–100 μM), glutamine (100–800 μM), and γ-aminobutyric acid (1–8 μM) in artificial sweat. Furthermore, the sensor demonstrated reliable performance in real human sweat samples (retaining >76% of the signal compared to artificial sweat), validating its feasibility for practical, noninvasive health monitoring. This capability stems from the hierarchical fern-shaped Au nanostructures, which provide abundant MIP binding sites to significantly broaden the detection range. This integrated sensor offers a viable approach for depression screening and a strategy for simultaneously detecting multiple metabolites with wide concentration disparities in biofluids.