Application of Distribution of Relaxation Times and Equivalent Circuit Modeling for Biosensor Electrode Development

The application of electrochemical impedance spectroscopy (EIS) for electrode characterization and biosensor development has become challenging due to the overlapping or superimposed semicircles and features on the Nyquist plot and numerous possible equivalent circuits. This study aimed to apply an EIS analysis workflow consisting of data validation using the Kramers–Kronig Model, distribution of relaxation times (DRT) analysis, and equivalent circuit model (ECM) parameterization using the recently available pyDRTtools and the Python package “impedance.py”. The effect of modifying the electrode with a metal organic framework – Cu‐BTC, graphite, and gold nanoparticles (AuNP) was studied by calculating the effective capacitance ( C eff ) and electrochemically active surface area (ECSA) from the ECM parameters. 60% Cu‐BTC mixed with graphite (v/v) showed the highest increase in the C eff and therefore the ECSA from 0.18 to 12.72 cm 2 . Electrodeposition of AuNP reduced this value to 0.31 cm 2 due to in‐between particle agglomeration. The final hybrid nanomaterial was composed of DNA tagged with ferrocene and thiol, AuNP, and a 60% Cu‐BTC and graphite mixture assembled on a glassy carbon electrode. DRT analysis was used to propose the data‐driven ECMs. Based on the root mean square error of each model circuit and the percent standard error for each parameter, the transmission line model has the best fit mathematically. However, a Randles circuit with a constant phase element and a custom circuit composed of two RC in series between a resistor and a Warburg element are practical to use for further biosensor development using this electrode assembly.