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Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times

Determination of the distribution function of relaxation times (DFRT) is an approach that gives us more detailed insight into system processes, which are not observable by simple electrochemical impedance spectroscopy (EIS) measurements. DFRT maps EIS data into a function containing the timescale ch...

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Autores principales: Žic, Mark, Pereverzyev, Sergiy, Subotić, Vanja, Pereverzyev, Sergei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6885029/
https://www.ncbi.nlm.nih.gov/pubmed/31839841
http://dx.doi.org/10.1007/s13137-019-0138-2
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author Žic, Mark
Pereverzyev, Sergiy
Subotić, Vanja
Pereverzyev, Sergei
author_facet Žic, Mark
Pereverzyev, Sergiy
Subotić, Vanja
Pereverzyev, Sergei
author_sort Žic, Mark
collection PubMed
description Determination of the distribution function of relaxation times (DFRT) is an approach that gives us more detailed insight into system processes, which are not observable by simple electrochemical impedance spectroscopy (EIS) measurements. DFRT maps EIS data into a function containing the timescale characteristics of the system under consideration. The extraction of such characteristics from noisy EIS measurements can be described by Fredholm integral equation of the first kind that is known to be ill-posed and can be treated only with regularization techniques. Moreover, since only a finite number of EIS data may actually be obtained, the above-mentioned equation appears as after application of a collocation method that needs to be combined with the regularization. In the present study, we discuss how a regularized collocation of DFRT problem can be implemented such that all appearing quantities allow symbolic computations as sums of table integrals. The proposed implementation of the regularized collocation is treated as a multi-parameter regularization. Another contribution of the present work is the adjustment of the previously proposed multiple parameter choice strategy to the context of DFRT problem. The resulting strategy is based on the aggregation of all computed regularized approximants, and can be in principle used in synergy with other methods for solving DFRT problem. We also report the results from the experiments that apply the synthetic data showing that the proposed technique successfully reproduced known exact DFRT. The data obtained by our techniques is also compared to data obtained by well-known DFRT software (DRTtools).
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spelling pubmed-68850292019-12-12 Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times Žic, Mark Pereverzyev, Sergiy Subotić, Vanja Pereverzyev, Sergei GEM Original Paper Determination of the distribution function of relaxation times (DFRT) is an approach that gives us more detailed insight into system processes, which are not observable by simple electrochemical impedance spectroscopy (EIS) measurements. DFRT maps EIS data into a function containing the timescale characteristics of the system under consideration. The extraction of such characteristics from noisy EIS measurements can be described by Fredholm integral equation of the first kind that is known to be ill-posed and can be treated only with regularization techniques. Moreover, since only a finite number of EIS data may actually be obtained, the above-mentioned equation appears as after application of a collocation method that needs to be combined with the regularization. In the present study, we discuss how a regularized collocation of DFRT problem can be implemented such that all appearing quantities allow symbolic computations as sums of table integrals. The proposed implementation of the regularized collocation is treated as a multi-parameter regularization. Another contribution of the present work is the adjustment of the previously proposed multiple parameter choice strategy to the context of DFRT problem. The resulting strategy is based on the aggregation of all computed regularized approximants, and can be in principle used in synergy with other methods for solving DFRT problem. We also report the results from the experiments that apply the synthetic data showing that the proposed technique successfully reproduced known exact DFRT. The data obtained by our techniques is also compared to data obtained by well-known DFRT software (DRTtools). Springer Berlin Heidelberg 2019-11-30 2020 /pmc/articles/PMC6885029/ /pubmed/31839841 http://dx.doi.org/10.1007/s13137-019-0138-2 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Žic, Mark
Pereverzyev, Sergiy
Subotić, Vanja
Pereverzyev, Sergei
Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title_full Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title_fullStr Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title_full_unstemmed Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title_short Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
title_sort adaptive multi-parameter regularization approach to construct the distribution function of relaxation times
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6885029/
https://www.ncbi.nlm.nih.gov/pubmed/31839841
http://dx.doi.org/10.1007/s13137-019-0138-2
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