Impedance fingerprinting of biofilm formation on PEDOT:PSS electrodes enables rapid detection of biofilm-associated urinary tract infection

Biofilm associated urinary tract infections (UTIs) are not routinely distinguished from planktonic infections in clinical diagnostics, despite their distinct physiological behavior and clinical outcomes. Here, we present an impedance based bioelectronic approach that enables functional differentiation of biofilm infection using frequency resolved electrochemical impedance spectroscopy (EIS) on PEDOT:PSS-coated gold electrodes. To establish these impedance signatures, electroactive ( Pseudomonas aeruginosa) and non-electroactive ( Escherichia coli ) biofilms were used as model systems. Distinct electrical fingerprints were identified across frequency domains, including a pronounced low frequency impedance decrease (ΔZ at 1 Hz ≈ −1000 Ω) associated with redox mediated electron transfer in electroactive biofilms, and a significant increase in charge transfer resistance (Rct ≈ 80 Ω) characteristic of insulating, EPS-rich non-electroactive biofilms, while negligible charge transfer resistance was observed in the absence of biofilm. Building on this framework, the impedance fingerprints were applied to clinical urine samples, enabling differentiation between biofilm and non biofilm associated UTIs, and healthy controls. Notably, most biofilm-associated UTI samples exhibited impedance signatures consistent with non-electroactive E. coli , the predominant uropathogen in UTI. Finally, introducing the urine samples into a PEDOT: PSS embedded biofilm-on-chip platform enabled continuous monitoring of biofilm formation from UTI samples and their response to antibiotic treatment, highlighting the ability to detect biofilm persistence and tolerance in clinical case under physiologically relevant environment. This work establishes impedance fingerprinting as a robust and translational strategy for the detection, classification, and functional assessment of biofilm associated UTIs, with strong potential for personalized antibiotic screening and clinical decision support