TY - JOUR
T1 - Analysis of large experimental datasets in electrochemical impedance spectroscopy
AU - Bondarenko, Alexander S.
N1 - Funding Information:
I would also like to thank my colleagues: Dr. Minghua Huang (CES, Ruhr-Universität Bochum) for the experimental data-set acquisition and Dr. John B. Henry (CES, Ruhr-Universität Bochum) for proofreading. I am also thankful to Dr. Genady Ragoisha (Belarusian State University, Belarus) and Mr. Balazs B. Berkes (Eötvös Loránd University, Hungary) for valuable comments and remarks about the performance of the hybrid algorithm. The developed approach and the hybrid algorithm have also been independently tested by Dr. Benjamin Sanchez (Universitat Politecnica de Catalunya, Spain, November–December 2011) using bio-impedance data acquired in vivo and by Mr. Balazs B. Berkes (Eötvös Loránd University, Hungary, January–September 2011) using different electrochemical systems. The work was supported in part by the EU and the state NRW in the framework of the HighTech.NRW program.
PY - 2012/9/19
Y1 - 2012/9/19
N2 - An approach for the analysis of large experimental datasets in electrochemical impedance spectroscopy (EIS) has been developed. The approach uses the idea of successive Bayesian estimation and splits the multidimensional EIS datasets into parts with reduced dimensionality. Afterwards, estimation of the parameters of the EIS-models is performed successively, from one part to another, using complex nonlinear least squares (CNLS) method. The results obtained on the previous step are used as a priori values (in the Bayesian form) for the analysis of the next part. To provide high stability of the sequential CNLS minimisation procedure, a new hybrid algorithm has been developed. This algorithm fits the datasets of reduced dimensionality to the selected EIS models, provides high stability of the fitting and allows semi-automatic data analysis on a reasonable timescale. The hybrid algorithm consists of two stages in which different zero-order optimisation strategies are used, reducing both the computational time and the probability to overlook the global optimum. The performance of the developed approach has been evaluated using (i) simulated large EIS dataset which represents a possible output of a scanning electrochemical impedance microscopy experiments, and (ii) experimental dataset, where EIS spectra were acquired as a function of the electrode potential and time. The developed data analysis strategy showed promise and can be further extended to other electroanalytical EIS applications which require multidimensional data analysis.
AB - An approach for the analysis of large experimental datasets in electrochemical impedance spectroscopy (EIS) has been developed. The approach uses the idea of successive Bayesian estimation and splits the multidimensional EIS datasets into parts with reduced dimensionality. Afterwards, estimation of the parameters of the EIS-models is performed successively, from one part to another, using complex nonlinear least squares (CNLS) method. The results obtained on the previous step are used as a priori values (in the Bayesian form) for the analysis of the next part. To provide high stability of the sequential CNLS minimisation procedure, a new hybrid algorithm has been developed. This algorithm fits the datasets of reduced dimensionality to the selected EIS models, provides high stability of the fitting and allows semi-automatic data analysis on a reasonable timescale. The hybrid algorithm consists of two stages in which different zero-order optimisation strategies are used, reducing both the computational time and the probability to overlook the global optimum. The performance of the developed approach has been evaluated using (i) simulated large EIS dataset which represents a possible output of a scanning electrochemical impedance microscopy experiments, and (ii) experimental dataset, where EIS spectra were acquired as a function of the electrode potential and time. The developed data analysis strategy showed promise and can be further extended to other electroanalytical EIS applications which require multidimensional data analysis.
KW - Complex nonlinear least squares method
KW - Electrochemical impedance spectroscopy
KW - Hybrid algorithms
KW - Multidimensional data analysis
KW - Successive Bayesian estimation
UR - http://www.scopus.com/inward/record.url?scp=84864854531&partnerID=8YFLogxK
U2 - 10.1016/j.aca.2012.06.055
DO - 10.1016/j.aca.2012.06.055
M3 - Article
AN - SCOPUS:84864854531
SN - 0003-2670
VL - 743
SP - 41
EP - 50
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -