Impedance-Based Estimation of Process Parameters in Electrolytic Systems via Circuit-Embedded Neural Networks (CENN) - DATA
Citation Link: https://doi.org/10.15480/882.16744
Type
Dataset
Date Issued
2026-03-04
Data Curator
Language
English
TORE-DOI
Abstract
The Data folder contains all data supporting the impedance-based estimation of electrolyte concentration and temperature in aqueous H₂SO₄ using nanoporous gold electrodes. It includes 260 electrochemical impedance spectroscopy (EIS) spectra measured across 20 concentrations (1–20 mM) and 13 temperatures (26–50 °C). Raw measurements are organized by concentration and temperature, together with equivalent-circuit fits (ZARC + transmission-line model) that yield parameters such as solution resistance, interfacial polarization, and porous transport. The folder also holds outputs from the Circuit-Embedded Neural Network (CENN) forward and inverse models, including impedance predictions, concentration and temperature estimates from full and reduced-frequency sets, and Jacobian-based sensitivity analysis that identifies informative frequency bands. Results for different frequency-selection strategies (single-band, multi-band, and full-spectrum) are provided, along with classical machine learning benchmarks (Ridge, SVR, GPR, MLP, etc.) for comparison. The data support the main findings of the paper: CENN achieves a forward RMSE of about 0.06 Ω, inverse estimation with mean absolute errors of roughly 0.10 mM for concentration and 1.0 °C for temperature on full spectra, and comparable accuracy (about 0.12 mM and 1.1 °C) using only three optimized frequency anchors, reducing acquisition time from minutes to about six seconds.
Subjects
electrochemical impedance spectroscopy
machine learning
nanoporous gold
temperature estimation
concentration estimation
statistical methods
DDC Class
541.37: Electrochemistry
Funding Organisations
More Funding Information
This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1615 – 503850735.