Title:

Maximizing Mutual Information of Impedance Spectroscopic Sensor Data

Electrochemical biosensing is currently seeing a surge of innovation due to improvements in materials, manufacturing processes, computing power, and analysis strategies. Advancements in computers and decoding methodologies typically go hand in hand. However, despite the increase in processing power, most electrochemists who utilize impedance spectroscopy have relied on the well-explored combination of a redox couple and a Randles circuit. The full potential of fitting equivalent circuits to novel systems has yet to be mapped out. We aim to develop a pipeline that searches for more complex circuits to fit a given dataset. Building up from a circuit-agnostic view, the distribution of relaxation times separates processes according to their individual timescales. Leveraging this approach, impedance spectroscopy can track distinct changes to complex systems such as electrode modification degradation and sleep stages. These changes can be compensated for, in the case of sensor fouling, and potentially adjusted through biofeedback.

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