Modeling of the impedance data of gadolinia doped ceria based actuators: a distribution function of relaxation times and machine learning approach

Abstract

Gadolinia-doped ceria is one of the most extensively examined oxide ion conductors to exhibit large nonclassical electrostriction. The electromechanical response depends on the grain and grain boundaries which can be probed using electrochemical impedance spectroscopy. In this study, we have modeled the impedance spectra from buckled and free standing gadolinia doped ceria (Gd0.2Ce0.8O1.9) membrane in Al/Ti/Gd0.2Ce0.8O1.9/Ti/Al electromechanical device, at different AC excitation voltages (2, 4, 6, 8 and 10 V), and starting from room temperature to 100 °C. The analysis of impedance spectrum is commonly done by equivalent circuit modeling to obtain the resistors ( R ) and capacitors ( C ). However, to overcome the inherent problems like non-uniqueness, over fitting, presently different approaches have been chosen such as impedance spectroscopy genetic programming (ISGP) and artificial neural networks (ANNs). ISGP finds an analytic form of the distribution function of relaxation times (DFRTs) using impedance spectra. The DFRT analysis reveals that the grain boundary capacitance for the membrane ∼10−8–10−9 F decreases with increasing temperature and excitation voltages. Besides, neural networks, which are optimized by Bayesian regularization, simulate both the real and imaginary parts of impedance following the pattern of experimental data up to several Hz. The DFRT analysis on this simulated impedance data shows an effect of grain boundaries. Indeed, ANNs as optimized by Levenberg-Marquardt method, can estimate the R and C ’s for present system with maximum relative errors of 24% and 22%, respectively.

Impedance Analysis of Electrochemical Systems

“…They trained machine learning models to recognize failures from a database of simulated EIS. Similar approaches have been applied for corrosion, − for ceramic actuators, and for batteries. − …”

Electrochemical Performance of Orthorhombic CsPbI3 Perovskite in Li-Ion Batteries

“…The distribution function of relaxation times (DFRTs) was calculated by the Impedance Spectroscopy Genetic Programming (ISGP) program using the impedance spectra after different cycles [ 39 , 40 , 41 , 42 , 43 , 44 ]. As the DFRT approach is applicable only in the Kramers–Krönig (KK) relations compatible regime, the supporting figure ( Supplementary Materials , Figure S19 ) suggests that we can safely identify three peaks within the KK compatible regime (around 0.1 Hz).…”

Using transfer-learning-based algorithms as data reduction strategies for volatile organic compounds classification using plasma spectroscopy

“…Machine learning (ML) has become increasingly popular in recent years across a variety of fields [8], including the analysis of images [9,10], videos [11], and spectra [12][13][14][15][16][17][18]. The features in the data can be successfully extracted using ML algorithms [19], and the extracted features can be used for regression [6,[20][21][22][23][24], classification [25,26], and error detection. In the past, deep learning was described as a data-driven and unexplainable black box.…”