Analysis of electrochemical impedance spectroscopy data for sputtered iridium oxide electrodes
<i>Objective</i>. Our objective was to perform a complete analysis of<i>in-vitro</i>impedance data for sputtered iridium oxide film (SIROF) micro-electrodes. The analysis included quantification of the stochastic and bias error structure and development of a process model that accounted for the chemistry and physics of the electrode-electrolyte interface.<i>Approach</i>. The measurement model program was used to analyze electrochemical impedance spectroscopy (EIS) data for SIROF micro-electrodes at potentials ranging from -0.4 to +0.6 V(Ag|AgCl). The frequency range used for the analysis was that determined to be consistent with the Kramers-Kronig relations. Interpretation of the data was enabled by truncating frequencies at which the ohmic impedance influenced the impedance.<i>Main results</i>. An interpretation model was developed that considered the impedance of the bare surface and the contribution of a porous component, based on the de Levie model of porous electrodes. The influence of iridium oxidation state on impedance was included. The proposed model fit all 36 EIS spectra well. The effective capacitance of the SIROF system ranged from 32 mF cm<sup>-2</sup>at -0.4 V(Ag|AgCl) to a maximum of 93 mF cm<sup>-2</sup>at 0.2 and 0.4 V(Ag|AgCl).<i>Significance</i>. The model developed to interpret the impedance response of neural stimulation electrodes<i>in vitro</i>guides model development for<i>in-vivo</i>studies.
Authors
- Kevin J. Otto (ORCID: https://orcid.org/0000-0002-2317-6194)
- Stuart F. Cogan (ORCID: https://orcid.org/0000-0001-6316-3032)
- Yupeng Wu (ORCID: https://orcid.org/0009-0001-4717-5217)
- Mark E. Orazem (ORCID: https://orcid.org/0000-0003-3668-7767)
- Cynthia C. Eluagu (ORCID: https://orcid.org/0009-0006-3857-8336)
- H. D. Lutz
Institutions
- The University of Texas at Dallas (US)
- Purdue University West Lafayette (US)
- University of Florida (US)
Publication Details
- Journal
- Journal of Neural Engineering
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1088/1741-2552/add090
- Citations
- 1
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.91
Funders
- National Institutes of Health