Frequency-domain analysis of voltammetric signals: a framework to augment electrochemical sensing explored through benzenediol detection

• Presents a frequency-structured framework for electrochemical measurement • Analyses the signal chain in reverse: output response, system transfer function, and input excitation. • Separates faradaic and capacitive processes using AC voltammetry and Randles circuit modelling. • Quantifies waveform spectra with frequency-based descriptors, coalescing incompatible time domain parameters. • Guides waveform tuning for improved faradaic clarity and measurement fidelity. Electrochemical signals are traditionally interpreted in the time domain, where overlapping faradaic and non-faradaic currents, noise, and drift obscure frequency-dependent behaviour. This study introduces a frequency-domain framework that complements time-domain analysis by decomposing voltammetric signals into their harmonic components through Fourier methods. AC voltammetry provides experimental evidence of how increasing excitation frequency progressively suppresses faradaic clarity, while a modified Randles equivalent-circuit model explains this response through the interplay of charge transfer, diffusion, and double-layer charging processes. Fourier series analysis of canonical voltammetric techniques, including linear sweep, cyclic, differential pulse, and square wave voltammetry, shows that waveform geometry uniquely defines harmonic structure. Fast Fourier transform analysis of practical data highlights artefacts introduced by finite sampling, binning, and spectral leakage, emphasising the need for conceptual awareness when interpreting experimental spectra. Quantitative spectral descriptors such as centroid, bandwidth, flatness, and low-frequency power fraction link waveform design directly to faradaic visibility and measurement clarity. Frequency-domain analysis therefore establishes that electrochemical measurement is inherently frequency structured. By combining experimental data, circuit modelling, and spectral metrics within a single framework, this approach provides a general route to optimise waveform parameters, reduce capacitive interference, and improve interpretability across electrochemical techniques.